fix type promotion for div in RemoveMixedTypeOperators #12157
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/12157
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 3 PendingAs of commit 6eddd12 with merge base f6bb143 ( NEW FAILURE - The following job has failed:
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The promotion strategy is dependent on the rounding mode (see the div decomp in PyTorch https://github.com/pytorch/pytorch/blob/main/torch/_refs/__init__.py#L1214 and then the promotion annotation on each of the true_divide/trunc_divide/floor_divide functions itcalls). I had to restructure the test a bit more so that lint didn't complain it was too complex. ghstack-source-id: 20618fd ghstack-comment-id: 3026116637 Pull-Request-resolved: #12157
This was referenced Jul 2, 2025
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| if ( | ||
| op == torch.ops.aten.div.Tensor_mode | ||
| and kwargs.get("rounding_mode") is None | ||
| ): | ||
| promotion_kind = ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT |
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Are you saying div.Tensor_mode without rounding_mode specified is equivalent to div.Tensor
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larryliu0820
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noting green tests except lintrunner |
swolchok
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The promotion strategy is dependent on the rounding mode (see the div decomp in PyTorch https://github.com/pytorch/pytorch/blob/main/torch/_refs/__init__.py#L1214 and then the promotion annotation on each of the true_divide/trunc_divide/floor_divide functions itcalls). I had to restructure the test a bit more so that lint didn't complain it was too complex. ghstack-source-id: 0d3aa92 ghstack-comment-id: 3026116637 Pull-Request-resolved: #12157
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pull / test-eval_llama-mmlu-linux / linux-job (pull_request) has previously passed and flaked this time, calling it good |
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swolchok
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The promotion strategy is dependent on the rounding mode (see the div decomp in PyTorch https://github.com/pytorch/pytorch/blob/main/torch/_refs/__init__.py#L1214 and then the promotion annotation on each of the true_divide/trunc_divide/floor_divide functions itcalls). I had to restructure the test a bit more so that lint didn't complain it was too complex. ghstack-source-id: b75ace2 ghstack-comment-id: 3026116637 Pull-Request-resolved: #12157
Tanish2101
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The promotion strategy is dependent on the rounding mode (see the div decomp in PyTorch https://github.com/pytorch/pytorch/blob/main/torch/_refs/__init__.py#L1214 and then the promotion annotation on each of the true_divide/trunc_divide/floor_divide functions itcalls). I had to restructure the test a bit more so that lint didn't complain it was too complex.
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The promotion strategy is dependent on the rounding mode (see the div
decomp in PyTorch
https://github.com/pytorch/pytorch/blob/main/torch/_refs/__init__.py#L1214
and then the promotion annotation on each of the
true_divide/trunc_divide/floor_divide functions itcalls).
I had to restructure the test a bit more so that lint didn't complain it was too complex.