tenstorrent/tt-metal
Oct 2024 – Sep 2025
11 Months active
Languages Used
C++PythonShellYAMLBash
Technical Skills
C++C++ developmentPyTorchPythonalgorithm optimizationcommand line interface (CLI) development
Evan Smal
PROFILE
Evan Smal
Esmal worked extensively on the tenstorrent/tt-metal repository, building scalable model inference features and optimizing distributed tensor operations for deep learning workloads. Over 11 months, they delivered robust Conv2D and MaxPool2d layers with sharding and configuration management, enhanced memory handling, and improved performance testing frameworks. Their technical approach combined C++ and Python development with CUDA and PyTorch integration, focusing on modular pipeline design, efficient data movement, and rigorous unit testing. By refactoring device pipelines, expanding CI/CD coverage, and implementing flexible tensor processing, Esmal improved reliability, throughput, and maintainability, enabling safer deployment and faster iteration for production machine learning models.
Overall Statistics
Feature vs Bugs
68%Features
Repository Contributions
128Total
Bugs
23
Commits
128
Features
50
Lines of code
15,854
Activity Months11
Your Network
742 people
Work History
September 2025
11 Commits • 3 Features
Sep 1, 2025September 2025: Delivered foundational TT-Metal improvements with a focus on scalable model inference, reliability, and developer productivity. Highlights include a new Conv2D layer with sharding and TTNN weights integrated into the configuration and builder flows, robust pooling support, and strengthened TT-CNN scaffolding. A targeted performance fix for UNet addressed a cq_id propagation regression, restoring peak efficiency. Expanded tests and documentation further improve usability and long-term maintainability, enabling faster deployment of distributed CNN workloads and safer configuration management.
11 Commits • 3 Features
Sep 1, 2025September 2025: Delivered foundational TT-Metal improvements with a focus on scalable model inference, reliability, and developer productivity. Highlights include a new Conv2D layer with sharding and TTNN weights integrated into the configuration and builder flows, robust pooling support, and strengthened TT-CNN scaffolding. A targeted performance fix for UNet addressed a cq_id propagation regression, restoring peak efficiency. Expanded tests and documentation further improve usability and long-term maintainability, enabling faster deployment of distributed CNN workloads and safer configuration management.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for tenstorrent/tt-metal focusing on delivering flexible tensor processing capabilities and improving configuration reliability. This month delivered a key feature enabling multi-output tensors and fixed a critical memory configuration error handling issue.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for tenstorrent/tt-metal focusing on delivering flexible tensor processing capabilities and improving configuration reliability. This month delivered a key feature enabling multi-output tensors and fixed a critical memory configuration error handling issue.
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 performance and impact summary for tenstorrent/tt-metal focusing on optimization of the Performance Testing Framework for UNet and YOLOv9c. Key design changes streamline inference benchmarking, improve event handling, output collection, and multi-device execution, and remove unnecessary tests to improve maintainability and measurement efficiency.
2 Commits • 1 Features
Jul 1, 2025July 2025 performance and impact summary for tenstorrent/tt-metal focusing on optimization of the Performance Testing Framework for UNet and YOLOv9c. Key design changes streamline inference benchmarking, improve event handling, output collection, and multi-device execution, and remove unnecessary tests to improve maintainability and measurement efficiency.
July 2025
June 2025
32 Commits • 16 Features
Jun 1, 2025June 2025 – Tenstorrent/tt-metal monthly summary focused on performance and reliability improvements across DRAM handling, device pipeline architecture, and CI stability. Delivered feature-rich changes to memory layout, model inference paths, and build workflows, with targeted bug fixes that improved robustness and developer velocity. The updates were implemented with attention to business value: higher throughput, lower memory footprint, broader hardware support, and streamlined release processes.
June 2025
32 Commits • 16 Features
Jun 1, 2025June 2025 – Tenstorrent/tt-metal monthly summary focused on performance and reliability improvements across DRAM handling, device pipeline architecture, and CI stability. Delivered feature-rich changes to memory layout, model inference paths, and build workflows, with targeted bug fixes that improved robustness and developer velocity. The updates were implemented with attention to business value: higher throughput, lower memory footprint, broader hardware support, and streamlined release processes.
May 2025
26 Commits • 12 Features
May 1, 2025May 2025 tt-metal monthly summary: Delivered substantial feature work, reliability improvements, and performance-focused optimizations across the repository. Implemented experimental channels-last memory layout support (ttnn.experimental.convert_to_hwc), with scaffolding and incremental stabilization culminating in a working implementation and a focused bug fix to address a dumb mistake. Added multi-tile support to broaden hardware compatibility, accompanied by a minor fix to ensure stability across tile configurations. Refactored runtime arguments to compile-time arguments to simplify configuration and reduce runtime overhead. Split the reader functionality into modular components and established a test suite to improve coverage for new/updated features. Executed a focused performance program, updating Mamba demo performance targets and applying general performance improvements, including Tensor constructor usage for clearer and faster tensor creation. Strengthened observability and reliability with tracing support and an experiment-running framework, plus parallel processing enhancements via shard weights. Also completed a series of quality improvements and maintenance tasks (code review responses, clang build include fixes, barrier relocation, copyright updates, and general bug fixes). Overall impact: higher compute throughput potential, easier experimentation, improved reliability, and a more scalable base ready for future hardware targets and production workloads.
26 Commits • 12 Features
May 1, 2025May 2025 tt-metal monthly summary: Delivered substantial feature work, reliability improvements, and performance-focused optimizations across the repository. Implemented experimental channels-last memory layout support (ttnn.experimental.convert_to_hwc), with scaffolding and incremental stabilization culminating in a working implementation and a focused bug fix to address a dumb mistake. Added multi-tile support to broaden hardware compatibility, accompanied by a minor fix to ensure stability across tile configurations. Refactored runtime arguments to compile-time arguments to simplify configuration and reduce runtime overhead. Split the reader functionality into modular components and established a test suite to improve coverage for new/updated features. Executed a focused performance program, updating Mamba demo performance targets and applying general performance improvements, including Tensor constructor usage for clearer and faster tensor creation. Strengthened observability and reliability with tracing support and an experiment-running framework, plus parallel processing enhancements via shard weights. Also completed a series of quality improvements and maintenance tasks (code review responses, clang build include fixes, barrier relocation, copyright updates, and general bug fixes). Overall impact: higher compute throughput potential, easier experimentation, improved reliability, and a more scalable base ready for future hardware targets and production workloads.
May 2025
April 2025
14 Commits • 3 Features
Apr 1, 2025April 2025 (Month: 2025-04) — Tenstorrent tt-metal: Delivered a focused set of CI/CD and test-suite improvements that tightened feedback loops, improved reliability, and expanded multi-architecture validation, enabling faster, safer releases. Key work spanned enhancements to the Sliding Window Test Suite CI pipeline, the introduction of nightly convolution testing, a comprehensive CI/CD refactor with matrix testing, and targeted stability fixes to the demo/test suite. The changes collectively reduce flaky tests, accelerate iteration cycles, and strengthen validation of performance-sensitive paths, aligning with business goals of faster release cadence and higher confidence in model-based workloads. Technologies/skills demonstrated include GitHub Actions CI/CD, YAML workflow orchestration, matrix-based parallel testing across architectures, test stability practices, and performance verification in CI pipelines.
April 2025
14 Commits • 3 Features
Apr 1, 2025April 2025 (Month: 2025-04) — Tenstorrent tt-metal: Delivered a focused set of CI/CD and test-suite improvements that tightened feedback loops, improved reliability, and expanded multi-architecture validation, enabling faster, safer releases. Key work spanned enhancements to the Sliding Window Test Suite CI pipeline, the introduction of nightly convolution testing, a comprehensive CI/CD refactor with matrix testing, and targeted stability fixes to the demo/test suite. The changes collectively reduce flaky tests, accelerate iteration cycles, and strengthen validation of performance-sensitive paths, aligning with business goals of faster release cadence and higher confidence in model-based workloads. Technologies/skills demonstrated include GitHub Actions CI/CD, YAML workflow orchestration, matrix-based parallel testing across architectures, test stability practices, and performance verification in CI pipelines.
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for tenstorrent/tt-metal focusing on sharded tensor robustness, input layout enhancements, and test stability. Delivered concrete fixes and features to improve correctness in distributed tensor operations, enabling more reliable performance workstreams and CI reliability.
4 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for tenstorrent/tt-metal focusing on sharded tensor robustness, input layout enhancements, and test stability. Delivered concrete fixes and features to improve correctness in distributed tensor operations, enabling more reliable performance workstreams and CI reliability.
February 2025
January 2025
19 Commits • 4 Features
Jan 1, 2025January 2025 performance highlights for tenstorrent/tt-metal. Key feature work centered on UNet Shallow model API and performance improvements (including CHW input/output support and device-level optimizations) complemented by robust testing, CI improvements, and trace/test updates. The month also delivered golden/reference implementations for core TTNN operations, expanded grouped tensor operations with enhanced debugging, critical resharding/memory-layout fixes, and improved performance reporting utilities. These efforts collectively improved production reliability, model throughput, and validation rigor, while expanding the capabilities essential for CHW-based workflows.
January 2025
19 Commits • 4 Features
Jan 1, 2025January 2025 performance highlights for tenstorrent/tt-metal. Key feature work centered on UNet Shallow model API and performance improvements (including CHW input/output support and device-level optimizations) complemented by robust testing, CI improvements, and trace/test updates. The month also delivered golden/reference implementations for core TTNN operations, expanded grouped tensor operations with enhanced debugging, critical resharding/memory-layout fixes, and improved performance reporting utilities. These efforts collectively improved production reliability, model throughput, and validation rigor, while expanding the capabilities essential for CHW-based workflows.
December 2024
6 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for tenstorrent/tt-metal focused on stabilizing memory usage, expanding tensor preprocessing capabilities, and strengthening the Stable Diffusion workflow. Key improvements include robust memory management for group normalization, enhanced handling of padded shards in convert_to_chw, preprocessing utilities for Conv2d/ConvTranspose2d, and CI/test suite stabilizations.
6 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for tenstorrent/tt-metal focused on stabilizing memory usage, expanding tensor preprocessing capabilities, and strengthening the Stable Diffusion workflow. Key improvements include robust memory management for group normalization, enhanced handling of padded shards in convert_to_chw, preprocessing utilities for Conv2d/ConvTranspose2d, and CI/test suite stabilizations.
December 2024
November 2024
5 Commits • 3 Features
Nov 1, 2024November 2024 was focused on delivering foundational improvements to tenstorrent/tt-metal that enable better multi-device scalability, more efficient data movement, and robust validation/test coverage. Work centered on grouped tensor operations, CNN channel reordering, and width-sharded tensor resharding, with a test baseline alignment reflecting updated model performance metrics.
November 2024
5 Commits • 3 Features
Nov 1, 2024November 2024 was focused on delivering foundational improvements to tenstorrent/tt-metal that enable better multi-device scalability, more efficient data movement, and robust validation/test coverage. Work centered on grouped tensor operations, CNN channel reordering, and width-sharded tensor resharding, with a test baseline alignment reflecting updated model performance metrics.
October 2024
7 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for tenstorrent/tt-metal. Delivered targeted performance and reliability improvements aimed at increasing throughput, debugging efficiency, and benchmarking realism. Notable outcomes include: UNet performance, optimization, and correctness enhancements (concurrent data transfers on a single CQ; folded batches into channels; tests for shallow grouped convolutions; validation to prevent garbage outputs) with commits bc40fbd3505ef45e3b1b0e146490137b49d71375; ff995bfc9d1f0c4da5a4ab6872b02cd8bc86c849; 37fc6b6acfa733ed81fddf420ef9197b94b3fb0f; 94f165109b316b3903cc3f9ea494f6777d347c0e. Also improved matrix multiplication error messaging (commit dfc7299dbb69c1c58b2d5855e019bdcc61dfa7ab). And benchmarking enhancements: CLI support for device ID and page size in read/write benchmarks and updated Mamba device performance targets (commits dacf8592d0624a10acbaab95098e2ab36ef2fffe; 0813bd38dd3405c002bd9bf0f37d7f889cec495d).
7 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for tenstorrent/tt-metal. Delivered targeted performance and reliability improvements aimed at increasing throughput, debugging efficiency, and benchmarking realism. Notable outcomes include: UNet performance, optimization, and correctness enhancements (concurrent data transfers on a single CQ; folded batches into channels; tests for shallow grouped convolutions; validation to prevent garbage outputs) with commits bc40fbd3505ef45e3b1b0e146490137b49d71375; ff995bfc9d1f0c4da5a4ab6872b02cd8bc86c849; 37fc6b6acfa733ed81fddf420ef9197b94b3fb0f; 94f165109b316b3903cc3f9ea494f6777d347c0e. Also improved matrix multiplication error messaging (commit dfc7299dbb69c1c58b2d5855e019bdcc61dfa7ab). And benchmarking enhancements: CLI support for device ID and page size in read/write benchmarks and updated Mamba device performance targets (commits dacf8592d0624a10acbaab95098e2ab36ef2fffe; 0813bd38dd3405c002bd9bf0f37d7f889cec495d).
October 2024
Activity
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Quality Metrics
Correctness89.6%
Maintainability84.2%
Architecture85.4%
Performance85.2%
AI Usage26.6%
Skills & Technologies
Programming Languages
BashC++PythonShellYAML
Technical Skills
API developmentAlgorithm designC++C++ developmentC++ programmingCI/CDCMakeCNNCUDACUDA ProgrammingComputer visionConcurrency handlingContinuous IntegrationConvolutional neural networksData Analysis
Repositories Contributed To
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