**Get device index by torch.privateuse1._utils._get_device_index, if the metched exists.**
Reason:
Can only get device_index 0 if ```location``` such as 'privateuse1' before modify.
Can get accurate deivce index use _get_device_index in this scenario.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108123
Approved by: https://github.com/albanD
Summary:
## Context
Both `aten.sum` and `aten.squeeze` have a "most generic" variant in the form of `aten.sum.dim_IntList` and `aten.squeeze.dims` respectively. Add decompositions for other non generic variants of these operators to express them using the most generic variant.
Note that to register these decomps, the reference implementation under `_refs` had to be removed as registered decompositions. cc: @lezcano @peterbell10
Test Plan: Github CI + Meta Internal CI
Differential Revision: D49965952
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110645
Approved by: https://github.com/peterbell10, https://github.com/digantdesai, https://github.com/manuelcandales
We want to get to a point where most `UserError`s link to `exportdb` examples. This PR makes passing case names non-optional to make this intent clearer and encourage developers who raise `UserError`s to make or point to examples that make fixing such errors more obvious for users.
In addition, sometimes there are multiple examples that are relevant to an error. Thus this PR also enables passing multiple case names.
Differential Revision: [D50020465](https://our.internmc.facebook.com/intern/diff/D50020465/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110733
Approved by: https://github.com/zhxchen17
We had the option but never used cpu_offload as optimizer state_dict offloads the tensors to CPU by default. And this is usually most users want as the tensors are required to be moved to CPU eventually. However, we may want to disable offloading to CPU in some cases, epsecially for the debugging purpose. This PR lets optimizer state_dict read the flag.
Differential Revision: [D48913340](https://our.internmc.facebook.com/intern/diff/D48913340/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108434
Approved by: https://github.com/wz337
This PR adds support for aten.where and support implicit scalar
promotion, basically when we meet scalar tensors in dispatching logic,
we implicitly convert it those to replicated dtensor
The latter also enables bunch of ops in op db to pass
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110584
Approved by: https://github.com/fduwjj
Summary:
As title, this diff hides the contiguous requirement for user input mesh when initializing DeviceMesh.
In the current implementation, when testing with inter-node model parallelism, an exception is thrown during mesh validation when the following input is provided:
```
mesh = torch.arange(0, world_size).view(mp_size, dp_size).transpose(0, 1)
device_mesh = DeviceMesh(
"cuda",
mesh.contiguous(),
mesh_dim_names=("dp", "mp")
)
```
Test Plan:
**Unit Test**:
```
buck2 test mode/dev-nosan //caffe2/test/distributed/_tensor:device_mesh -- test_validate_device_mesh
Test UI: https://www.internalfb.com/intern/testinfra/testrun/3940649876878399
Network: Up: 0B Down: 0B
Jobs completed: 6. Time elapsed: 1:58.7s.
Tests finished: Pass 1. Fail 0. Fatal 0. Skip 0. Build failure 0
```
**Test with MP**
```
mesh = torch.arange(0, world_size).view(mp_size, dp_size).transpose(0, 1)
device_mesh = DeviceMesh(
"cuda",
mesh.contiguous(),
mesh_dim_names=("dp", "mp")
)
```
Without the change: exception.
After this change: initialzied sucessfully.
Differential Revision: D49942839
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110628
Approved by: https://github.com/wanchaol, https://github.com/xw285cornell, https://github.com/fduwjj
Previously,`Dim` definitions that shared the same name but had different ranges were allowed to appear in the `dynamic_shapes` argument of an `export` call. They would correspond to the *same* dynamic dimension (identified by the shared name) with an effective range would be the *intersection* of the different ranges.
However this behavior can be confusing, because having different definitions with the same name is more likely than not unintentional. Therefore, this PR makes it a user error.
We still allow different definitions with the same name to exist at the same time (no global uniqueness) as long as they are not confused in the same `export` call. Redefinitions with the same bounds are also allowed, in case they are accidentally created by executing the same code multiple times.
Differential Revision: [D49965944](https://our.internmc.facebook.com/intern/diff/D49965944/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110638
Approved by: https://github.com/zhxchen17
While the `range_constraints` that is initially derived by processing of constraints only contains symbols that appear in the graph module, eventually the `range_constraints` that are in the exported program seem to contain more symbols than those that appear in the graph module. Clearly this is a regression, because the example of "Expressing Dynamism" in our public docs (https://pytorch.org/docs/stable/export.html#expressing-dynamism) does not show the extra symbols in `range_constraints`, but running the example does.
The problem seems to arise when we are running `_transform` passes, where we regenerate the `range_constraints` from the `shape_env`. However, as a rule, symbols that have `replacements` are actually replaced (by other expressions, including constants or other symbols), so they should never appear in the graph module. Thus we can filter such symbols out from `range_constraints` as well.
Differential Revision: [D49969620](https://our.internmc.facebook.com/intern/diff/D49969620/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110644
Approved by: https://github.com/zhxchen17
Summary: The runtime assertions inserted in the `torch._export.export` by the `_AddRuntimeAssertionsForInlineConstraintsPass` lead to errors in AOT Inductor like #109884. In `torch._export.aot_compile` export and AOT compilation are run consecutively which would lead to the above issue if any assertions are inserted.
In this PR, we're adding a new parameter / flag to `torch._export.aot_compile`, `remove_runtime_assertions`, to remove the assertions inserted during export before AOT compilation. The flag is set to `False` for BC.
Additionally, we remove the flag `add_runtime_assertions_for_inline_constraints` recently added to `torch._dynamo.config`, as it can lead to undesirable `torch._export` behavior and is 's no longer required for the AOT Inductor testing purposes.
Test Plan: CI
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110710
Approved by: https://github.com/zhxchen17, https://github.com/chenyang78
Expose a set of observability hooks into C10D such that our users can
detect collectives failure both faster and more easily.
The design is similar to NCCL desync debug that it minimized the
overhead by doing most of the work out of the main thread.
This PR introduces a new module torch.distributed.hooks that exposes the following set of methods:
register_collective_start_hook
register_collective_end_hook
register_process_group_hook
The process group hook exposes PG creation on the member ranks and call them inline from the
the PG creation code. This is fine since this happens during initialization and a limited number of times.
The collective start/end hooks are fired from a single background thread. It reads
events from a C++ queue and dispatches over.
Queue notification is oddly done using a pipe, this is needed so python can abort the thread on shutdown
and have it as background thread. This is not possible with more reasonable choices like a condvar.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108815
Approved by: https://github.com/wconstab, https://github.com/fduwjj
This pr expose torch._higher_order_ops.cond as torch.cond.
1. Need to add #noqa: F811 to the _check calls in torch/__init__.py to address some confusing linter error "Redefinition of unused 'cond'" but only one cond is imported and for these lines that have this error, they don't define the cond but just use it as an argument.
2. Also add cond to the list that allows it to be traced through so as dynamo could trigger the CondHigherOrder logic instead of creating a TorchVariable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110293
Approved by: https://github.com/zou3519
Fixes recent broken unit tests caused by PR #109908 because cudnn and miopen have separate batch norm functions.
```
2023-10-05T09:35:01.6606614Z _______________ TestQuantizePT2EQAT.test_qat_conv_bn_fusion_cuda _______________
2023-10-05T09:35:01.6606948Z Traceback (most recent call last):
2023-10-05T09:35:01.6607362Z File "/var/lib/jenkins/pytorch/test/quantization/pt2e/test_quantize_pt2e_qat.py", line 323, in test_qat_conv_bn_fusion_cuda
2023-10-05T09:35:01.6607767Z self._verify_symmetric_xnnpack_qat_graph(
2023-10-05T09:35:01.6608217Z File "/var/lib/jenkins/pytorch/test/quantization/pt2e/test_quantize_pt2e_qat.py", line 130, in _verify_symmetric_xnnpack_qat_graph
2023-10-05T09:35:01.6608658Z self._verify_symmetric_xnnpack_qat_graph_helper(
2023-10-05T09:35:01.6609105Z File "/var/lib/jenkins/pytorch/test/quantization/pt2e/test_quantize_pt2e_qat.py", line 173, in _verify_symmetric_xnnpack_qat_graph_helper
2023-10-05T09:35:01.6609623Z m = prepare_qat_pt2e(m, quantizer)
2023-10-05T09:35:01.6610171Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/quantize_pt2e.py", line 178, in prepare_qat_pt2e
2023-10-05T09:35:01.6610561Z _fuse_conv_bn_qat(model)
2023-10-05T09:35:01.6611072Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/pt2e/qat_utils.py", line 501, in _fuse_conv_bn_qat
2023-10-05T09:35:01.6611497Z m = _fuse_conv_bn_qat_helper(m, is_cuda=True)
2023-10-05T09:35:01.6612065Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/pt2e/qat_utils.py", line 575, in _fuse_conv_bn_qat_helper
2023-10-05T09:35:01.6612492Z _get_conv_bn_getitem_nodes(r.replacements)
2023-10-05T09:35:01.6613058Z File "/opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/ao/quantization/pt2e/qat_utils.py", line 383, in _get_conv_bn_getitem_nodes
2023-10-05T09:35:01.6613465Z assert bn_node is not None
2023-10-05T09:35:01.6613716Z AssertionError
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110653
Approved by: https://github.com/jerryzh168, https://github.com/pruthvistony
Add non-package python modules to the public API checks.
The original change is to remove the `ispkg` check in this line
https://github.com/pytorch/pytorch/blob/main/docs/source/conf.py#L518
Everything else is to add the appropriate modules to the rst files, make sure every module we provide can be imported (fixed by either making optional dependencies optional or just deleting files that have been un-importable for 3 years), make API that are both modules and functions (like torch.autograd.gradcheck) properly rendered on the docs website without confusion and add every non-documented API to the allow list (~3k of them).
Next steps will be to try and fix these missing docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110568
Approved by: https://github.com/zou3519
Adam part of: https://github.com/pytorch/pytorch/issues/110506
TODO:
- If this approach is validated as a good one, it an also be applied to all other optimizers which convert `complex` via list comprehensions
### Results:
`NUM_PARAMS=200, foreach=True`
- main: dynamo: 43s, inductor: 31s, total: 74s
- this PR: dynamo: 3.5s, inductor: 30s, total: 34s (dynamo speedup: 12.3x, overall speedup: 34s, 2.1x)
`NUM_PARAMS=1000, foreach=True, has_complex shortcut`:
```
<class 'torch.optim.adam.Adam'> {'lr': 0.01, 'foreach': True} torch.float32 TorchDynamo compilation metrics:
Function Runtimes (s)
------------------------------------ -------------------------------
_compile.<locals>.compile_inner 0.0329, 50.0806, 0.0041
OutputGraph.call_user_compiler 44.9924
```
`NUM_PARAMS=1000, foreach=True`:
```
<class 'torch.optim.adam.Adam'> {'lr': 0.01, 'foreach': True} torch.float32 TorchDynamo compilation metrics:
Function Runtimes (s)
------------------------------------ -------------------------------
_compile.<locals>.compile_inner 0.0389, 58.6069, 0.0043
OutputGraph.call_user_compiler 44.1425
```
### Discussion
- `has_complex` shortcut provides additional 2x dynamo speedup. It is not necessary to achieve a significant overall speedup.
CC: @janeyx99 @mlazos
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110607
Approved by: https://github.com/janeyx99, https://github.com/lezcano
Summary:
Sometimes local_shards are empty on some ranks, and out.dtype is float16, which will cause error if enforce_dtype is True because `data` will be float32.
Callers know best what dtype they want, so we can just let callers decide.
Temporarily keep enforce_dtype for backward compatibility
Test Plan: Run local and MAST job
Reviewed By: uciyc123
Differential Revision: D46886551
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110561
Approved by: https://github.com/wanchaol, https://github.com/malfet
Summary:
Since we changed IR that we are working with to pre autograd aten IR, it's easier
to use plain pattern match instead of relying on source_matcher_utils now, this
PR refactors the annotation for conv to use aten ops directly.
Also fixed reentrant test after this change.
Test Plan:
python test/test_quantization.py TestQuantizePT2E
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110308
Approved by: https://github.com/kimishpatel
When we convert to local tensor, dtensor can't track autograd or
gradient layout of the local tensor anymore, if user do sth not expected, there
needs to be a way for user to hint about the gradient layout of the
local tensor
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110629
Approved by: https://github.com/zdevito
Summary:
Original commit changeset: 03980fb054d5
Original Phabricator Diff: D49519512
Bisecting shows that this diff is the cause of S369683. Since this affects Ads production, need to back out this diff immediately.
Test Plan: See S369683
Reviewed By: ezyang
Differential Revision: D49958638
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110622
Approved by: https://github.com/yanboliang
To reduce the amount of logs
* for successes, only print the part that says what tests ran and don't print the rest. Zip the log into an artifact. The line listing al the test names is really long, but if you view source of the raw logs, it will not wrap so it will only be one line. The log classifier can also be configured to ignored this line. Gets rid of lines like `test_ops.py::TestCommonCPU::test_multiple_devices_round_cpu_int64 SKIPPED [0.0010s] (Only runs on cuda) [ 9%]`
* for failures/reruns, print logs. Do not zip.
Also
* change log artifact name
Examples of various logs:
a074db0f7f failures
1b439e24c4 failures
possibly controversial haha
should i include an option for always printing?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110033
Approved by: https://github.com/huydhn
Summary:
D49187352 caused our model conversion and loading of QAT checkpoint to be stuck with thrift time out.
we are actively checking in final code and model for static quant HTP prod model, and encountered this breakage at head Thursday.
Thrift timeout is a not failing, and because of that, it's hard to bisect and find this culprit. It is also hard to set up unit test, because the job simply time-out. Better test is needed to guard downstream model conversion against upstream changes.
Our suspicion of why this diff broke us is that we create a lot of modules with qat (in a recursive manner) but our model is not a qat traceable module (it is a graph with many qat modules and floating point modules). With fuctools.partial as in the original diff, we will be caching modules in the memory and causing the memory of the machine to be taken up completely.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110392
Approved by: https://github.com/junesg, https://github.com/jerryzh168
Defining kernels as static vars is problematic for subsequent model loading on non-default CUDA devices.
Assuming those kernels were loaded in context of the device #0, so, they are not nullptr anymore, therefore kernels won't work on devices other than the device #0.
This change makes devices remembered at model level in AOT mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110554
Approved by: https://github.com/chenyang78, https://github.com/desertfire
Fixes#106698
Also added a check for python API, because current error message
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/sehoon/pytorch-latest/torch/nn/modules/adaptive.py", line 128, in __init__
or (min(cutoffs) <= 0) \
ValueError: min() arg is an empty sequence
```
is not very comprehensible.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106777
Approved by: https://github.com/albanD
Ideally all `_dynamo.exc.UserError`s should have "case names", i.e., link to examples in `exportdb`.
This PR adds case names to several instances of `_dynamo.exc.UserError`. In particular, looking at coverage based on `UserErrorType`:
* `DYNAMIC_CONTROL_FLOW`, `ANTI_PATTERN`, and `STANDARD_LIBRARY` are fully covered.
* `CONSTRAINT_VIOLATION` and `DYNAMIC_DIM` have no coverage. We don't seem to have any dedicated examples of specifying dynamic shapes in `exportdb` (although they are used in some other examples without explanation, to avoid some specialization that would make such examples moot).
* `INVALID_INPUT` is only partly covered. Frankly this is tedious to cover via examples.
Differential Revision: [D49928518](https://our.internmc.facebook.com/intern/diff/D49928518/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110555
Approved by: https://github.com/angelayi, https://github.com/ydwu4
Summary: Today, we get different batch norm ops depending on
the device the model is placed on at export time. Exporting
`model.cpu()` gives `_native_batch_norm_legit`, while exporting
`model.cuda()` gives `cudnn_batch_norm`. QAT fusion currently
only supports the former and silently ignores the latter. This
commit fixes this by additionally matching on the latter op
during QAT fusion.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT.test_qat_conv_bn_fusion
python test/test_quantization.py TestQuantizePT2EQAT.test_qat_conv_bn_relu_fusion
Reviewers: jerryzh168, kimishpatel
Subscribers: jerryzh168, kimishpatel, supriyar
Differential Revision: [D49615145](https://our.internmc.facebook.com/intern/diff/D49615145)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109908
Approved by: https://github.com/jerryzh168
There is an issue with float8 type promotion, because _promoteTypesLookup doesn't contain records for few types between bfloat16 and float8.
I have simply moved float8 types just after bfloat16, however I'm not sure if it doesn't break serialization.
Please, decide if it can stay like this, or should I insert missing records filled with "ud" into _promoteTypesLookup instead of moving types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110279
Approved by: https://github.com/albanD
Part of #109684
- #109684
Changes:
- Add new functions `tree_structure`, `tree_leaves`, `tree_map_` and `tree_map_only_` to Python pytree.
- Extract reusable tests for pytree to `TestGenericPytree`.
- Change `treespec_dumps` and `treespec_loads` in C++ pytree to call Python pytree and use JSON string as serialization type.
- Rename `torch.utils.pytree` -> `torch.utils._cxx_pytree`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110395
Approved by: https://github.com/zou3519
We want to be able to use SingletonSymNode to represent strides for Jagged layout tensor. The following is for 3D, but easily generalizable to higher dimensions.
Constraints:
- [B, x, D] (where x represents the "variably lengthed dim") can be strided in two ways [x, 1, sum(x)] and [dx, d, 1]. We need two different placeholder values depending on how the jagged tensor is strided.
- When doing operations we need the strides of output tensors to be expressable in terms of the strides and sizes of the inner tensors. Given [B, x, D] @ [D, D'], the output strides is [x * D', D', 1] rather than some opaque [x2, D', 1]. This constraint exists because if I'm tracing, I need a symint to represent the output stride. This symint needs to come from somewhere; I get it in several ways: (1) create a constant, (2) unbacked symint, (3) create a new input using a source, (4) output of an operation on an existing symint. It is clear that (4) is what we want here, which brings us to the design below.
Design:
Given the two constraints, the most straightforward way to implement this is actually to update SingletonSymNode to include some scalar factor, i.e. Morally, SingletonSymNode represents `factor * [s_0, s_1, …, s_n]` This enables us to symbolically compute strides from sizes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110369
Approved by: https://github.com/ezyang
ghstack dependencies: #110044
When mapping between the original signature of a program and the graph-captured signature of its exported program, we emit errors when we see unexpected original or graph-captured inputs or outputs.
These errors can arise because of various reasons, e.g.:
1. some input or output has been lifted because of mutation
2. some type is not pytree-registered for flattening / unflattening
3. some type cannot be realized with graph operations
(This is probably not an exhaustive list.)
Previously we used to emit errors based on a vanilla id-based membership check between the two sides, mostly anticipating (1) as the reason for errors. But this does not do justice to errors because of (2) or (3).
This PR emits a different error when it finds (3) to be a probable cause. Specifically, it considers only Tensor and Sym* types to be "supported": no other type seems to be realizable by graph operations.
When (2) is a probable cause, we sometimes also hit the same error because we would expect the supported types to show through upon registration. But this kind of error may need some more work in the future.
Differential Revision: [D49885828](https://our.internmc.facebook.com/intern/diff/D49885828/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110472
Approved by: https://github.com/ydwu4
Summary:
This PR removed several APIs from the AOTInductor interface,
which are not used by the client.
It also simplified AOTInductor's model class by removing
the dim info for input/output tensors. We included dim info
before to return max output shapes, which was used by the client
to allocate memory for output tensors. Now, we allocate output
tensor memory from the .so so that we don't need to maintain
such information any more. The deletion of dim info from
the model class also simplified the codegen quite a bit.
Test Plan: ci
Reviewed By: khabinov
Differential Revision: D49835430
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110411
Approved by: https://github.com/khabinov, https://github.com/desertfire, https://github.com/jansel
Summary: Registering param/buffer will write into a vector inside Object, need to maintain thread safety if we have threads reading from the vector and writing to the vector at the same time.
Test Plan: CI
Differential Revision: D49882601
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110488
Approved by: https://github.com/davidberard98
Summary: This diff refactors the code by moving CUDAAllocatorConfig into the header file. This config refactoring is done so that we can use the same config code for CUDA pinned memory as well.
Test Plan: sandcastle
Differential Revision: D49653265
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110123
Approved by: https://github.com/zdevito
Summary:
See wrapper.codegen_reinterpret_view(), it return a temporary handle for tensor, which has following problem.
```
# NB, the return handle here represents a temporary tensor, which will be automatically
# released.
# Here's a sample usage in the cpp wrapper code:
# ```
# aoti_torch_addmm_out(
# buf1,
# arg1_1,
# RAIIAtenTensorHandle(tmp_tensor_handle_0),
# buf0,
# 1L,
# 1L));
# ```
# RAIIAtenTensorHandle(tmp_tensor_handle_0) will be released after the call to addmm_out.
# This could be problematic when it's used in a different pattern, for example:
# ````
# AtenTensorHandle tensor_args[] = {RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6};
# aoti_torch_proxy_executor_call_function(..., tensor_args);
# ````
# RAIIAtenTensorHandle(tmp_tensor_handle_2) will be invalid when it's used in the latter
# kernel call.
return f"RAIIAtenTensorHandle({tmp_name})"
```
As a result, ProxyExecutor would generate following code, which cause invalid memory access.
Before:
```
// Source Nodes: [fn_with_tuple_output], Original ATen: [fb.fn_with_tuple_output]
AtenTensorHandle tmp_tensor_handle_2;
AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch__reinterpret_tensor(buf3, 2, int_array_0, int_array_1, 0L, &tmp_tensor_handle_2));
...
AtenTensorHandle tensor_args[] = {RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6};
int64_t int_args[] = {1};
aoti_torch_proxy_executor_call_function(proxy_executor, 1, 1, int_args, 3, tensor_args);
buf3.reset();
```
With fix in this diff, ProxyExecutor generates following code
After:
```
// Source Nodes: [fn_with_tuple_output], Original ATen: [fb.fn_with_tuple_output]
AtenTensorHandle tmp_tensor_handle_2;
AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch__reinterpret_tensor(buf3, 2, int_array_0, int_array_1, 0L, &tmp_tensor_handle_2));
...
aoti_torch_proxy_executor_call_function(proxy_executor, 1, 1, std::vector<int64_t>{1}.data(), 3, std::vector<AtenTensorHandle>{RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6}.data());
buf3.reset();
```
I am not exactly a big fan of such `std::vector{...}.data()` for creating a temp array, but I can't think of another fix.
Test Plan: buck2 run mode/dev-nosan deeplearning/aot_inductor/test:test_custom_ops
Reviewed By: desertfire
Differential Revision: D49758764
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110451
Approved by: https://github.com/desertfire
Summary: Point to point ops don't enqueue their work to the `workMetaList_` which means that the NCCL watchdog does not watch over them, hence they do not respect the collective timeouts.
Test Plan:
While trying to add a test I found we dont have tests which validate the nccl watch dog. It looks like this is because we dont have a good way to detect when nccl watchdog has thrown an error (exception is thrown in a side thread) in our testing framework / `MultiprocessTestCase`
I manually tested this change with the script in https://github.com/pytorch/pytorch/issues/109401, but need to look more closely at how to automate a test for NCCL watchdog
Differential Revision: D49418976
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109611
Approved by: https://github.com/wconstab
Triplet Margin Loss takes in a Callable `distance_function` parameter which is not supported as an argument on the fx graph. See previous error:
> File "/scratch/eellison/work/pytorch/torch/_dynamo/symbolic_convert.py", line 562, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/scratch/eellison/work/pytorch/torch/_dynamo/variables/torch.py", line 723, in call_function
*proxy_args_kwargs(args, kwargs),
File "/scratch/eellison/work/pytorch/torch/_dynamo/utils.py", line 504, in proxy_args_kwargs
f"call_function args: {typestr(*args)} {typestr(*list(kwargs.values()))}"
File "/scratch/eellison/work/pytorch/torch/_dynamo/exc.py", line 143, in unimplemented
raise Unsupported(msg)
torch._dynamo.exc.Unsupported: call_function args: TensorVariable() TensorVariable() TensorVariable() ConstantVariable(float) NNModuleVariable()
This is fixable by just inlining into `triplet_margin_loss` and continuing to compile it. This required support for `has_torch_function_variadic`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110302
Approved by: https://github.com/mlazos
The first reland broke internal (failing diff: D49617462).
The major error looks like it's because there's an internal-only higher order op that needs a new functionalization rule. I'm going to land an internal diff for that and confirm tests pass before relanding this PR.
Also confirmed that the issue from https://github.com/pytorch/pytorch/issues/110121 is fixed, and added a test.
This reverts commit 1b90f07f5a.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110079
Approved by: https://github.com/ezyang
Summary: This diff fixes a heap underflow found by fuzzing in torch/csrc/jit/runtime/vararg_functions.cpp
Test Plan:
CI and
```
arc lionhead crash reproduce 1753074381791061
```
doesn't crash anymore.
Differential Revision: D49537535
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110441
Approved by: https://github.com/Skylion007
Summary:
Previously, we link against cuda libs even for pure cpp backend.
This caused issues for cases where the inference platform does not
have GPUs. This diff removed cuda dependency for cpp backend.
Reviewed By: bertmaher, muchulee8, mikekgfb
Differential Revision: D49800712
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110409
Approved by: https://github.com/bertmaher, https://github.com/desertfire
Description:
- Fixed misleading test sample case
Context: sample input is composed of input tensor `(N, C, iH, iW)` and grid tensor `(N, oH, oW, 2)`, however, grid is defined as `(N, C, oW, 2)`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110383
Approved by: https://github.com/peterbell10
**Background**: recordStreams can result in memory spikes, so we don't want them to appear in FSDP (https://dev-discuss.pytorch.org/t/fsdp-cudacachingallocator-an-outsider-newb-perspective/1486). @ awgu is working on fixing this, but it turns out profiler was causing recordStream to get called when it is enabled.
Why profiler was causing recordStream to get called: NCCL calls add profiler events manually; they register a callback to be executed when the future for the collective is completed; this indicates the end of the CPU-side profiler event for the callback:
c2c7c4035f/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp (L1822-L1824)
In order to guarantee safety, ivalue::Future::invokeCallback calls `recordStream` on the future's storage buffers; this marks the fact that other streams (e.g. the one that the callback runs on) may need to use the storage.
c2c7c4035f/aten/src/ATen/core/ivalue_inl.h (L1171-L1173)
**Change**: The end-profiler-event callback doesn't actually use the future, so we don't need to recordStream on it. This PR introduces an optional parameter `uses_future` for adding callbacks; a user can set this variable to "false" to unsafely skip the recordStream, if the user knows that the future will not be used in the lambda.
**Tests**: (a) unit tests; (b) added an assert in recordStream: c2c7c4035f/c10/cuda/CUDACachingAllocator.cpp (L3260) and verified that it doesn't get triggered when running basic distributed tests w/ profiler enabled
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109933
Approved by: https://github.com/wconstab
Summary: after converting nn.multihead attention we weren't deleting the
old in_proj_weight and in_proj_bias despite not (really) using them.
Test Plan: python test/test_quantization.py -k
"test_custom_module_multi_head_attention"
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110407
Approved by: https://github.com/jerryzh168
This PR updates backend as a property to DTensorTestbase and add "cpu:gloo,cuda:nccl" support in DTensorTestbase so that we can use `cpu:gloo,cuda:nccl` backend for checkpoint unit tests.
cc. @wanchaol, @fduwjj, @XilunWu
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110397
Approved by: https://github.com/wanchaol
Partially fixes `test_memory_format_factory_like_functions_preserve` with PYTORCH_TEST_WITH_INDUCTOR. Inductor preserves memory layouts for user-visible outputs as annotated on the fx graph that it is passed in. That graph is generated from running aot_autograd with decompositions. If the decompositions give incorrect strides, so will inductor.
This preserves the layout of `_like` operators when it corresponds to a `torch.memory_format`. It doesnt fix a) arbitrary permutations, b) striding of non-dense outputs. Both of these are lower-pri compared to preserving channels last. We would need either https://github.com/pytorch/pytorch/issues/92920 or a `to` variant that takes in a physical layout arbitrary permutations. I converted the output of rand to the correct layout instead of passing the layout in so that this would compose with the `replace_random` pass, and because the two pointwise ops will get fused anyway.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110242
Approved by: https://github.com/int3
