Fix object-based collectives API to use torch.cuda.current_device instead of rank #46897
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…tead of rank These APIs implicitly assumed that gpu for rank == rank index, but that is not necessarily true. For example, the first GPU could be used for a different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we mandate that the user specify the device to use via `torch.cuda.set_device()` before making calls to this API. This expectation should be okay since we clearly document it, and we expect the user to set this for DistributedDataParallel as well. Also adds/tidies up some documentation. Differential Revision: [D24556177](https://our.internmc.facebook.com/intern/diff/D24556177/) [ghstack-poisoned]
rohan-varma
requested review from
apaszke,
mingzhe09088,
mrshenli,
pietern,
pritamdamania87 and
zhaojuanmao
as code owners
facebook-github-bot
added
the
oncall: distributed
…_device instead of rank" rank** These APIs implicitly assumed that gpu for rank == rank index, but that is not necessarily true. For example, the first GPU could be used for a different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we mandate that the user specify the device to use via `torch.cuda.set_device()` before making calls to this API. This expectation should be okay since we clearly document it, and we expect the user to set this for DistributedDataParallel as well. Also adds/tidies up some documentation. Differential Revision: [D24556177](https://our.internmc.facebook.com/intern/diff/D24556177/) [ghstack-poisoned]
rohan-varma
added a commit
that referenced
this pull request
Oct 27, 2020
…tead of rank Pull Request resolved: #46897 These APIs implicitly assumed that gpu for rank == rank index, but that is not necessarily true. For example, the first GPU could be used for a different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we mandate that the user specify the device to use via `torch.cuda.set_device()` before making calls to this API. This expectation should be okay since we clearly document it, and we expect the user to set this for DistributedDataParallel as well. Also adds/tidies up some documentation. ghstack-source-id: 115223259 Differential Revision: [D24556177](https://our.internmc.facebook.com/intern/diff/D24556177/)
💊 CI failures summary and remediationsAs of commit b062f49 (more details on the Dr. CI page):
🕵️ 1 new failure recognized by patternsThe following CI failures do not appear to be due to upstream breakages:
|
| Job | Step | Action |
|---|---|---|
 pytorch_linux_xenial_cuda10_2_cudnn7_py3_gcc7_test1 |
Report results | 🔁 rerun |
ci.pytorch.org: 1 failed
This comment was automatically generated by Dr. CI (expand for details).
Follow this link to opt-out of these comments for your Pull Requests.Please report bugs/suggestions on the GitHub issue tracker or post in the (internal) Dr. CI Users group.
This comment has been revised 14 times.
…_device instead of rank" rank** rank** These APIs implicitly assumed that gpu for rank == rank index, but that is not necessarily true. For example, the first GPU could be used for a different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we mandate that the user specify the device to use via `torch.cuda.set_device()` before making calls to this API. This expectation should be okay since we clearly document it, and we expect the user to set this for DistributedDataParallel as well. Also adds/tidies up some documentation. Differential Revision: [D24556177](https://our.internmc.facebook.com/intern/diff/D24556177/) [ghstack-poisoned]
rohan-varma
added a commit
that referenced
this pull request
Oct 27, 2020
…tead of rank Pull Request resolved: #46897 These APIs implicitly assumed that gpu for rank == rank index, but that is not necessarily true. For example, the first GPU could be used for a different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we mandate that the user specify the device to use via `torch.cuda.set_device()` before making calls to this API. This expectation should be okay since we clearly document it, and we expect the user to set this for DistributedDataParallel as well. Also adds/tidies up some documentation. ghstack-source-id: 115266274 Differential Revision: [D24556177](https://our.internmc.facebook.com/intern/diff/D24556177/)
mrshenli
approved these changes
Oct 27, 2020
| # true. | ||
| current_device = torch.cuda.current_device() | ||
| input_tensor = input_tensor.to(current_device) | ||
| local_size = local_size.to(current_device) | ||
| # Gather all local sizes. This is so that we can find the max size, and index | ||
| # until the correct size when deserializing the tensors. | ||
| group_size = get_world_size(group=group) | ||
| object_sizes_tensor = torch.zeros(group_size, dtype=int).to( |
There was a problem hiding this comment.
nit: we can avoid a copy by directly creating the tensor on the desired device?
There was a problem hiding this comment.
yes, good catch. Will fix all these callsites.
| @@ -1400,7 +1412,7 @@ def all_gather_object(object_list, obj, group=group.WORLD): | |||
| input_tensor.resize_(max_object_size) | |||
| coalesced_output_tensor = torch.empty( | |||
| max_object_size * group_size, dtype=torch.uint8 | |||
| ).to(my_rank if is_nccl_backend else "cpu") | |||
| ).to(torch.cuda.current_device() if is_nccl_backend else "cpu") | |||
There was a problem hiding this comment.
nit: use a var to dedup this if-else and also merge it with the if is_nccl_backend clause? E.g.:
current_device = torch.device("cpu")
if is_nccl_backend:
current_device = torch.cuda.current_device()
input_tensor = input_tensor.to(current_device)
...
object_sizes_tensor = torch.zeros(group_size, dtype=int, device=current_device)
...| @@ -1475,7 +1491,7 @@ def gather_object(obj, object_gather_list=None, dst=0, group=group.WORLD): | |||
| if my_rank == dst: | |||
| coalesced_output_tensor = torch.empty( | |||
| max_object_size * group_size, dtype=torch.uint8 | |||
| ).to(my_rank if is_nccl_backend else "cpu") | |||
| ).to(torch.cuda.current_device() if is_nccl_backend else "cpu") | |||
Codecov Report
@@ Coverage Diff @@
## gh/rohan-varma/190/base #46897 +/- ##
===========================================================
- Coverage 68.96% 68.88% -0.09%
===========================================================
Files 434 434
Lines 56219 56128 -91
===========================================================
- Hits 38771 38661 -110
- Misses 17448 17467 +19 |
…_device instead of rank" rank** rank** rank** These APIs implicitly assumed that gpu for rank == rank index, but that is not necessarily true. For example, the first GPU could be used for a different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we mandate that the user specify the device to use via `torch.cuda.set_device()` before making calls to this API. This expectation should be okay since we clearly document it, and we expect the user to set this for DistributedDataParallel as well. Backwards compatibility is not an issue since these APIs have not been publicly announced yet. Also adds/tidies up some documentation. Differential Revision: [D24556177](https://our.internmc.facebook.com/intern/diff/D24556177/) [ghstack-poisoned]
rohan-varma
added a commit
that referenced
this pull request
Oct 28, 2020
…tead of rank Pull Request resolved: #46897 These APIs implicitly assumed that gpu for rank == rank index, but that is not necessarily true. For example, the first GPU could be used for a different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we mandate that the user specify the device to use via `torch.cuda.set_device()` before making calls to this API. This expectation should be okay since we clearly document it, and we expect the user to set this for DistributedDataParallel as well. Also adds/tidies up some documentation. ghstack-source-id: 115359633 Differential Revision: [D24556177](https://our.internmc.facebook.com/intern/diff/D24556177/)
|
CI errors are unrelated, landing |
|
This pull request has been merged in c7183c9. |
|
This pull request has been merged in c7183c9. |
4 tasks
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Stack from ghstack:
rank
rank**
rank**
rank**
These APIs implicitly assumed that gpu for rank == rank index, but
that is not necessarily true. For example, the first GPU could be used for a
different purpose and rank 0 could use GPU 1, rank 1 uses GPU 2, etc. Thus, we
mandate that the user specify the device to use via
torch.cuda.set_device()before making calls to this API. This expectation should be okay since we
clearly document it, and we expect the user to set this for
DistributedDataParallel as well. Backwards compatibility is not an issue since these APIs have not been publicly announced yet.
Also adds/tidies up some documentation.
Differential Revision: D24556177