Use parallel parsing at all stages#21266
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ilevkivskyi merged 4 commits intopython:masterfrom Apr 20, 2026
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JukkaL
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I found a potential perf issue.
| needs_parse.append(state) | ||
| # Set data that is needed to be written to cache meta. | ||
| state.known_suppressed_deps_opts = suppressed_deps_opts | ||
| # Performa actual parsing in parallel (but we don't need to compute dependencies). |
| for state in parallel_states: | ||
| assert state.tree is not None | ||
| if state.id in parallel_parsed_states: | ||
| if state in parallel_parsed_states: |
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O(n) contains check. Previously this was a dict so it used to be O(1). This loop is now O(n**2).
JukkaL
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According to mypy_primer, this change doesn't affect type check results on a corpus of open source code. ✅ |
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This is a follow-up to #21175 and #21119.
Right now we are only using parallel parsing during initial graph loading. This PR allows using it when processing SCCs that are stale due to dependencies (these are not parsed on initial loading). Implementation is a bit less trivial than I thought, because we need to completely extract the tree de-serialization (which is not parallelizeable). It is still relatively straightforward. The new logic is like this:
parse(eager=False)(default) we always return a serialized tree.parse(eager=True)a caller can force immediate de-serialization (when suitable), this will triggerload_from_raw().imports_only=True) one can useparse(eager=False)followed byload_from_raw()directly.cc @JukkaL