FixOp(embeddign_bag) change to use nn.functional.embeding_bag function | feat(torchlib) #1067
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| per_sample_weights = op.Expand(1, op.Shape(indices_1d)) | ||
| # Dtype of per_sample_weights is the same as weight | ||
| per_sample_weights = op.CastLike(per_sample_weights, weight) | ||
| result, offset2bag, bag_size, max_indices = _aten_embedding_bag_onnx( |
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| per_sample_weights = op.Expand(1, op.Shape(indices_1d)) | ||
| # Dtype of per_sample_weights is the same as weight | ||
| per_sample_weights = op.CastLike(per_sample_weights, weight) | ||
| result, offset2bag, bag_size, max_indices = _aten_embedding_bag_onnx( |
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| per_sample_weights = op.Expand(1, op.Shape(indices_1d)) | ||
| # Dtype of per_sample_weights is the same as weight | ||
| per_sample_weights = op.CastLike(per_sample_weights, weight) | ||
| result, offset2bag, bag_size, max_indices = _aten_embedding_bag_onnx( |
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PYLINT/W0612
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| # if per_sample_weights is None: | ||
| # # Set per_sample_weights to 1.0, because cannot check 'None' in ONNX-Script | ||
| # # Size of persample_weights is the same as indices, and should be 1d tensor | ||
| # indices_1d = op.Reshape(indices, [-1]) | ||
| # per_sample_weights = op.Expand(1, op.Shape(indices_1d)) | ||
| # # Dtype of per_sample_weights is the same as weight | ||
| # per_sample_weights = op.CastLike(per_sample_weights, weight) |
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Commented-out code
Codecov Report
@@ Coverage Diff @@
## main #1067 +/- ##
==========================================
- Coverage 77.94% 77.76% -0.18%
==========================================
Files 115 115
Lines 14684 14706 +22
Branches 1558 1563 +5
==========================================
- Hits 11445 11436 -9
- Misses 2871 2901 +30
- Partials 368 369 +1
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Test Results 18 files ± 0 18 suites ±0 1h 13m 51s ⏱️ + 6m 21s For more details on these failures and errors, see this check. Results for commit 1121b4d. ± Comparison against base commit b6c5e3b. This pull request removes 4 and adds 5 tests. Note that renamed tests count towards both.♻️ This comment has been updated with latest results. |
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We should still return four outputs and test using the aten op as explained in teams |
justinchuby
requested changes
Sep 25, 2023
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Not this guess, try another way. |
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For issue #1056, no idea what test case it is using and which target function is using ( nn.functional.embedding_bag or ops.aten.embedding_bag?)
This PR changed to use nn.functional.embedding_bag as reference.
The difference are: