[WIP] test if short circuite is material #153431
Open
Conversation
This file contains hidden or 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
[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/153431
Note: Links to docs will display an error until the docs builds have been completed. This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This PR needs a
|
laithsakka
added a commit
that referenced
this pull request
May 13, 2025
…_meta short circuit" * This verifies that the check short circuit is not material. #153431 I still need to add a unit test but verified that this works just need to add it as unit test. ``` import torch from torch.export import Dim, export class MyModel(torch.nn.Module): def forward(self, x, ranks): first_k = ranks.max().item() torch._check_is_size(first_k) narrow = x.narrow(dim = 1, start = 0, length = first_k) lt = narrow < narrow.size(1) return lt inps = ( torch.randn((8, 16), device="cuda"), torch.arange(8, device="cuda", dtype=torch.int8) ) spec = { "x": (Dim.AUTO, Dim.AUTO), "ranks": (Dim.AUTO,), } traced = export(MyModel(), inps, dynamic_shapes=spec, strict=True).run_decompositions({}) ``` [ghstack-poisoned]
laithsakka
added a commit
that referenced
this pull request
May 13, 2025
…uit" * This verifies that the check short circuit is not material. #153431 I still need to add a unit test but verified that this works just need to add it as unit test. ``` import torch from torch.export import Dim, export class MyModel(torch.nn.Module): def forward(self, x, ranks): first_k = ranks.max().item() torch._check_is_size(first_k) narrow = x.narrow(dim = 1, start = 0, length = first_k) lt = narrow < narrow.size(1) return lt inps = ( torch.randn((8, 16), device="cuda"), torch.arange(8, device="cuda", dtype=torch.int8) ) spec = { "x": (Dim.AUTO, Dim.AUTO), "ranks": (Dim.AUTO,), } traced = export(MyModel(), inps, dynamic_shapes=spec, strict=True).run_decompositions({}) ``` [ghstack-poisoned]
laithsakka
added a commit
that referenced
this pull request
May 14, 2025
…_meta short circuit" * This verifies that the check short circuit is not material. #153431 ``` import torch from torch.export import Dim, export class MyModel(torch.nn.Module): def forward(self, x, ranks): first_k = ranks.max().item() torch._check_is_size(first_k) narrow = x.narrow(dim = 1, start = 0, length = first_k) lt = narrow < narrow.size(1) return lt inps = ( torch.randn((8, 16), device="cuda"), torch.arange(8, device="cuda", dtype=torch.int8) ) spec = { "x": (Dim.AUTO, Dim.AUTO), "ranks": (Dim.AUTO,), } traced = export(MyModel(), inps, dynamic_shapes=spec, strict=True).run_decompositions({}) ``` [ghstack-poisoned]
laithsakka
added a commit
that referenced
this pull request
May 14, 2025
…uit" * This verifies that the check short circuit is not material. #153431 ``` import torch from torch.export import Dim, export class MyModel(torch.nn.Module): def forward(self, x, ranks): first_k = ranks.max().item() torch._check_is_size(first_k) narrow = x.narrow(dim = 1, start = 0, length = first_k) lt = narrow < narrow.size(1) return lt inps = ( torch.randn((8, 16), device="cuda"), torch.arange(8, device="cuda", dtype=torch.int8) ) spec = { "x": (Dim.AUTO, Dim.AUTO), "ranks": (Dim.AUTO,), } traced = export(MyModel(), inps, dynamic_shapes=spec, strict=True).run_decompositions({}) ``` [ghstack-poisoned]
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 (oldest at bottom):