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When using `from_config()` with DeepSpeed ZeRO-3, `_init_weights()` silently operated on partitioned empty tensors, making custom initialization a no-op. Parameters retained PyTorch's default kaiming_uniform_ instead of the intended initialization, causing abnormally large gradients and loss. The fix suppresses init during construction via `no_init_weights()`, then re-initializes module-by-module using `GatheredParameters` so each module's parameters are gathered before init runs. Co-Authored-By: Masahiro Tanaka <tohtana@users.noreply.github.com>
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winglian
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Feb 24, 2026
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zvik
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* fix zero3 init config * Fix ZeRO-3 weight initialization in `_from_config()` When using `from_config()` with DeepSpeed ZeRO-3, `_init_weights()` silently operated on partitioned empty tensors, making custom initialization a no-op. Parameters retained PyTorch's default kaiming_uniform_ instead of the intended initialization, causing abnormally large gradients and loss. The fix suppresses init during construction via `no_init_weights()`, then re-initializes module-by-module using `GatheredParameters` so each module's parameters are gathered before init runs. Co-Authored-By: Masahiro Tanaka <tohtana@users.noreply.github.com> * Apply repo consistency fixes --------- Co-authored-by: Masahiro Tanaka <tohtana@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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What does this PR do?
Supersedes #43847
When using zero3 + from_config, the model was incorrectly initialized as we were not gathering the params. Added a test also.
cc @tohtana