tenstorrent/tt-mlir
Mar 2026 – Apr 2026
2 Months active
Languages Used
MLIRPython
Technical Skills
Collective CommunicationMachine LearningPythonPython DevelopmentPython ScriptingSoftware Development
Aditi Pandey
PROFILE
Aditi Pandey
Ankit Pandey contributed to the tenstorrent/tt-mlir repository by developing multi-device support and distributed tensor operations for scalable machine learning inference. He implemented dynamic mesh shape selection and shard-aware tensor construction in Python and MLIR, enabling robust output handling and correct reconstruction of presharded tensors across multi-chip configurations. Ankit also expanded TTNNBuilder with collective communication primitives such as AllGather, DistributeTensor, AggregateTensor, and reduce_scatter, introducing new APIs, golden mapping utilities, and comprehensive test coverage. His work addressed memory management, end-to-end validation, and regression safety, demonstrating depth in distributed computing, backend development, and tensor manipulation for reliable multi-node deployments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
1,183
Activity Months2
Your Network
438 peopleWork History
April 2026
1 Commits • 1 Features
Apr 1, 2026Delivered TTNN Builder reduce_scatter support in the TT-MLIR project for April 2026, enabling scalable distributed tensor reductions within multi-node deployments. Implemented a new TTNN builder API, golden mapping utility, and comprehensive tests to validate performance and correctness. Included an MLIR golden test snippet to verify the operation end-to-end. Committed work tied to ticket #7625 (f211ca30d17dd5bc0ac371a519916a166880e7e1) addressing the absence of reduce_scatter support.
1 Commits • 1 Features
Apr 1, 2026Delivered TTNN Builder reduce_scatter support in the TT-MLIR project for April 2026, enabling scalable distributed tensor reductions within multi-node deployments. Implemented a new TTNN builder API, golden mapping utility, and comprehensive tests to validate performance and correctness. Included an MLIR golden test snippet to verify the operation end-to-end. Committed work tied to ticket #7625 (f211ca30d17dd5bc0ac371a519916a166880e7e1) addressing the absence of reduce_scatter support.
April 2026
March 2026
4 Commits • 2 Features
Mar 1, 2026March 2026 performance highlights: Delivered essential multi-device support and distributed tensor capabilities for tt-mlir, elevating scalability, performance, and reliability across multi-chip configurations. Key outcomes include robust multi-device ttrt run output handling, and comprehensive TTNNBuilder collective communication operations, underpinned by improved testing and tooling.
March 2026
4 Commits • 2 Features
Mar 1, 2026March 2026 performance highlights: Delivered essential multi-device support and distributed tensor capabilities for tt-mlir, elevating scalability, performance, and reliability across multi-chip configurations. Key outcomes include robust multi-device ttrt run output handling, and comprehensive TTNNBuilder collective communication operations, underpinned by improved testing and tooling.
Activity
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Quality Metrics
Correctness92.0%
Maintainability80.0%
Architecture84.0%
Performance80.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
MLIRPython
Technical Skills
Collective CommunicationDistributed ComputingMachine LearningPythonPython DevelopmentPython ScriptingSoftware DevelopmentTensorFlowbackend developmenttensor manipulation
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