Fix numpy_helper to_array #6638
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Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com>
Codecov ReportAttention: Patch coverage is
✅ All tests successful. No failed tests found.
Additional details and impacted files@@ Coverage Diff @@
## main #6638 +/- ##
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+ Coverage 57.44% 57.46% +0.02%
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Files 507 507
Lines 31612 31584 -28
Branches 3058 3046 -12
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- Hits 18158 18151 -7
+ Misses 12610 12607 -3
+ Partials 844 826 -18 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com>
justinchuby
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Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com>
gramalingam
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### Description Refactor `to_array` to simplify and fix conversion logic. - The `_to_array` function was renamed back to `to_array`, consolidating with existing logic that leverages custom dtypes. - Created an unpack_4bit helper to unpack packed 4bit dtype data, replacing usage of the 4bit->float32 conversion functions because the original logic was not actually needed. - Removed test skips because bugs were fixed. - Type casting functions for custom dtypes will be marked private in the next PR. Public versions of them will be scheduled for deprecation. (#6639) Users should use other libraries like ml_dtypes for efficient type conversion in numpy. ### Motivation and Context Fix #6604 --------- Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com> Signed-off-by: Andreas Fehlner <fehlner@arcor.de>
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### Description Refactor `to_array` to simplify and fix conversion logic. - The `_to_array` function was renamed back to `to_array`, consolidating with existing logic that leverages custom dtypes. - Created an unpack_4bit helper to unpack packed 4bit dtype data, replacing usage of the 4bit->float32 conversion functions because the original logic was not actually needed. - Removed test skips because bugs were fixed. - Type casting functions for custom dtypes will be marked private in the next PR. Public versions of them will be scheduled for deprecation. (onnx#6639) Users should use other libraries like ml_dtypes for efficient type conversion in numpy. ### Motivation and Context Fix onnx#6604 --------- Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com> Signed-off-by: seungwoo-ji <seungwoo.ji@nuvilab.com>
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Description
Refactor
to_arrayto simplify and fix conversion logic._to_arrayfunction was renamed back toto_array, consolidating with existing logic that leverages custom dtypes.Motivation and Context
Fix #6604