Fix BinaryOp broadcasting for packed dim #2653
Closed
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
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2653
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit 0d2f6f7 with merge base a3bf63b ( BROKEN TRUNK - The following job failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
facebook-github-bot
added
the
CLA Signed
|
This pull request was exported from Phabricator. Differential Revision: D55278527 |
|
This pull request was exported from Phabricator. Differential Revision: D55278527 |
jorgep31415
force-pushed
the
export-D55278527
branch
from
1f16f5e to
482476b
Compare
jorgep31415
added a commit
to jorgep31415/executorch
that referenced
this pull request
Mar 25, 2024
Summary: Pull Request resolved: pytorch#2653 As copyrightly pointed out, broadcasting was not working properly for the example below. I root caused the to confusion between `sizes()` vs `gpu_sizes()` once again! These concepts are explained in pytorch#2520 We should use the CPU size, not the GPU size to detect when we should broadcast across the packed-dim texel's elements. # Example Given inputs `torch.ones(2, 3)` and `torch.ones(2, 1)` and `GPUMemoryLayout::WIDTH_PACKED`, we have CPU widths 3 and 1, respectively. These are aligned up to GPU widths 4 and 4, and hence we were failing to broadcast along the packed-dim texel's elements. ## torch.ones(2, 3) ``` (2, 3) = (H, W) = sizes [[1 1 1] [1 1 1]] -> (W, H) = (3, 2) → (4, 2) = gpu_sizes -> extents = (1, 2) [1 1 1 0] [1 1 1 0] ``` ## torch.ones(2, 1) ``` (2, 1) = (H, W) = sizes [[1] [1]] -> (W, H) = (1, 2) → (4, 2) = gpu_sizes -> extents = (1, 2) [1 0 0 0] [1 0 0 0] -> (broadcast from this change) [1 1 1 1] [1 1 1 1] ``` ## torch.ones(2, 3) + torch.ones(2, 1) Ignore the final element of each texel as it's just padding we never read. ``` No broadcast: [1 1 1 0] [1 1 1 0] + [1 0 0 0] [1 0 0 0] = [2 1 1 0] [2 1 1 0] Broadcast: [1 1 1 0] [1 1 1 0] + [1 1 1 1] [1 1 1 1] = [2 2 2 1] [2 2 2 1] ``` # Cleanup Remove unneeded `check_broadcastable()` since this is caught earlier in the PyTorch compiler pipeline. For example, `torch.ones(2, 3) + torch.ones(2, 2)` triggers this error: ``` TorchRuntimeError: Failed running call_function <built-in function add>(*(FakeTensor(..., size=(2, 3)), FakeTensor(..., size=(2, 2))), **{}): Attempting to broadcast a dimension of length 2 at -1! Mismatching argument at index 1 had torch.Size([2, 2]); but expected shape should be broadcastable to [2, 3] ``` Differential Revision: D55278527
jorgep31415
force-pushed
the
export-D55278527
branch
from
482476b to
4794269
Compare
jorgep31415
added a commit
to jorgep31415/executorch
that referenced
this pull request
Mar 25, 2024
Summary: As copyrightly pointed out, broadcasting was not working properly for the example below. I root caused the to confusion between `sizes()` vs `gpu_sizes()` once again! These concepts are explained in pytorch#2520 We should use the CPU size, not the GPU size to detect when we should broadcast across the packed-dim texel's elements. # Example Given inputs `torch.ones(2, 3)` and `torch.ones(2, 1)` and `GPUMemoryLayout::WIDTH_PACKED`, we have CPU widths 3 and 1, respectively. These are aligned up to GPU widths 4 and 4, and hence we were failing to broadcast along the packed-dim texel's elements. ## torch.ones(2, 3) ``` (2, 3) = (H, W) = sizes [[1 1 1] [1 1 1]] -> (W, H) = (3, 2) → (4, 2) = gpu_sizes -> extents = (1, 2) [1 1 1 0] [1 1 1 0] ``` ## torch.ones(2, 1) ``` (2, 1) = (H, W) = sizes [[1] [1]] -> (W, H) = (1, 2) → (4, 2) = gpu_sizes -> extents = (1, 2) [1 0 0 0] [1 0 0 0] -> (broadcast from this change) [1 1 1 1] [1 1 1 1] ``` ## torch.ones(2, 3) + torch.ones(2, 1) Ignore the final element of each texel as it's just padding we never read. ``` No broadcast: [1 1 1 0] [1 1 1 0] + [1 0 0 0] [1 0 0 0] = [2 1 1 0] [2 1 1 0] Broadcast: [1 1 1 0] [1 1 1 0] + [1 1 1 1] [1 1 1 1] = [2 2 2 1] [2 2 2 1] ``` # Cleanup Remove unneeded `check_broadcastable()` since this is caught earlier in the PyTorch compiler pipeline. For example, `torch.ones(2, 3) + torch.ones(2, 2)` triggers this error: ``` TorchRuntimeError: Failed running call_function <built-in function add>(*(FakeTensor(..., size=(2, 3)), FakeTensor(..., size=(2, 2))), **{}): Attempting to broadcast a dimension of length 2 at -1! Mismatching argument at index 1 had torch.Size([2, 2]); but expected shape should be broadcastable to [2, 3] ``` Differential Revision: D55278527
|
This pull request was exported from Phabricator. Differential Revision: D55278527 |
Summary: Pull Request resolved: pytorch#2653 As copyrightly pointed out, broadcasting was not working properly for the example below. I root caused the to confusion between `sizes()` vs `gpu_sizes()` once again! These concepts are explained in pytorch#2520 We should use the CPU size, not the GPU size to detect when we should broadcast across the packed-dim texel's elements. # Example Given inputs `torch.ones(2, 3)` and `torch.ones(2, 1)` and `GPUMemoryLayout::WIDTH_PACKED`, we have CPU widths 3 and 1, respectively. These are aligned up to GPU widths 4 and 4, and hence we were failing to broadcast along the packed-dim texel's elements. ## torch.ones(2, 3) ``` (2, 3) = (H, W) = sizes [[1 1 1] [1 1 1]] -> (W, H) = (3, 2) → (4, 2) = gpu_sizes -> extents = (1, 2) [1 1 1 0] [1 1 1 0] ``` ## torch.ones(2, 1) ``` (2, 1) = (H, W) = sizes [[1] [1]] -> (W, H) = (1, 2) → (4, 2) = gpu_sizes -> extents = (1, 2) [1 0 0 0] [1 0 0 0] -> (broadcast from this change) [1 1 1 1] [1 1 1 1] ``` ## torch.ones(2, 3) + torch.ones(2, 1) Ignore the final element of each texel as it's just padding we never read. ``` No broadcast: [1 1 1 0] [1 1 1 0] + [1 0 0 0] [1 0 0 0] = [2 1 1 0] [2 1 1 0] Broadcast: [1 1 1 0] [1 1 1 0] + [1 1 1 1] [1 1 1 1] = [2 2 2 1] [2 2 2 1] ``` # Cleanup Remove unneeded `check_broadcastable()` since this is caught earlier in the PyTorch compiler pipeline. For example, `torch.ones(2, 3) + torch.ones(2, 2)` triggers this error: ``` TorchRuntimeError: Failed running call_function <built-in function add>(*(FakeTensor(..., size=(2, 3)), FakeTensor(..., size=(2, 2))), **{}): Attempting to broadcast a dimension of length 2 at -1! Mismatching argument at index 1 had torch.Size([2, 2]); but expected shape should be broadcastable to [2, 3] ``` Differential Revision: D55278527
|
This pull request was exported from Phabricator. Differential Revision: D55278527 |
jorgep31415
force-pushed
the
export-D55278527
branch
from
4794269 to
0d2f6f7
Compare
SS-JIA
approved these changes
Mar 27, 2024
|
This pull request has been merged in 25c5b67. |
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.
Summary:
As copyrightly pointed out, broadcasting was not working properly for the example below. I root caused the to confusion between
sizes()vsgpu_sizes()once again! These concepts are explained in #2520We should use the CPU size, not the GPU size to detect when we should broadcast across the packed-dim texel's elements.
Example
Given inputs
torch.ones(2, 3)andtorch.ones(2, 1)andGPUMemoryLayout::WIDTH_PACKED, we have CPU widths 3 and 1, respectively. These are aligned up to GPU widths 4 and 4, and hence we were failing to broadcast along the packed-dim texel's elements.torch.ones(2, 3)
torch.ones(2, 1)
torch.ones(2, 3) + torch.ones(2, 1)
Ignore the final element of each texel as it's just padding we never read.
Cleanup
Remove unneeded
check_broadcastable()since this is caught earlier in the PyTorch compiler pipeline. For example,torch.ones(2, 3) + torch.ones(2, 2)triggers this error:Differential Revision: D55278527