
In September 2025, Srdjan Djordjevic enhanced the tenstorrent/tt-metal repository by implementing flexible tensor shape support within the Pybind module for ttnn.Tensor. He removed the previous hard-coded 4D shape constraint, introducing a vector-based shape representation at the Python-C++ interface. This approach allowed dynamic tensor shapes, improving integration with diverse models and pipelines while reducing shape-related errors. Srdjan’s work focused on bindings development using C++ and Python, resulting in a more usable and adaptable API for tensor construction. The depth of the change addressed a core limitation, enabling broader downstream compatibility without introducing new bugs during the release period.
September 2025 monthly summary for tenstorrent/tt-metal: Focused on delivering flexible tensor shape support in the Pybind module for ttnn.Tensor, enabling dynamic shapes and broader integration with models and pipelines. The change removes the hard-coded 4D shape constraint by adopting a vector-based shape representation in the Python-C++ boundary, improving usability and reducing shape-related errors across downstream components.
September 2025 monthly summary for tenstorrent/tt-metal: Focused on delivering flexible tensor shape support in the Pybind module for ttnn.Tensor, enabling dynamic shapes and broader integration with models and pipelines. The change removes the hard-coded 4D shape constraint by adopting a vector-based shape representation in the Python-C++ boundary, improving usability and reducing shape-related errors across downstream components.

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