modularml/mojo
Mar 2025 – Oct 2025
8 Months active
Languages Used
MojoPythonBazelType HintingYAML
Technical Skills
AI AccelerationAttention MechanismsC++ (implied)CUDACUDA/GPU ProgrammingCache Management
Shouzheng Liu
PROFILE
Shouzheng Liu
Over eight months, Sliu developed advanced distributed AI infrastructure in the modularml/mojo repository, focusing on scalable Mixture of Experts (MoE) and attention mechanisms for large language models. Sliu engineered core kernels and pipelines for GPU-accelerated inference and training, integrating CUDA and Python to optimize performance, memory efficiency, and reliability. Their work included implementing FP8 quantization, distributed tensor parallelism, and expert parallelism using SHMEM for cross-device communication. By building robust APIs, enhancing kernel correctness, and supporting non-uniform model architectures, Sliu enabled efficient, production-ready deployment of large models. The engineering demonstrated deep expertise in low-level optimization and distributed systems design.
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
137Total
Bugs
18
Commits
137
Features
59
Lines of code
25,611
Activity Months8
Your Network
198 people
Work History
October 2025
13 Commits • 1 Features
Oct 1, 2025October 2025—modularml/mojo: Delivering scalable Expert Parallelism (EP) for distributed MoE/inference, strengthening performance, reliability, and model coverage across our platform. Key features delivered: - End-to-end EP for distributed MoE/inference: initialization infra, dispatch and combine pipelines, MoE sharding, token format abstractions with FP8 quantization, and integration with DeepSeek-V3; enabling direct device-to-device communication and optimized EP kernel paths across distributed GPUs. - EP integration across core modules: EP support added to the MoE SDK/module, DeepSeek-V3 model compatibility, and FP8-based token encoding through TokenFormat traits; introduced BlockwiseFP8TokenFormat for ep_dispatch kernels. - Kernel and execution flow improvements: wired up EP dispatch and combine kernels and completed the EPBatchManager with combine-related functionality to streamline end-to-end EP execution. Major bugs fixed: - Stabilized SHMEM tests and ensured correctness across test suites; robustified grouped_matmul tests; fixed static stride usage in the Gumbel kernel; improved per-device chunked request handling in data-parallel text generation. - Additional test improvements and bug fixes aimed at improving scheduling accuracy and overall runtime robustness. Overall impact and accomplishments: - Significantly improved scalability and throughput for distributed MoE workloads; introduced a robust EP-enabled path with better cross-GPU communication, reduced test flakiness, and enhanced scheduling stability for DP workloads. - Expanded model coverage with EP-enabled DeepSeek-V3 integration, broadening applicability of the EP workflow across production models. Technologies/skills demonstrated: - Distributed systems, MoE architecture, FP8 quantization, token format abstractions, and kernel-level EP optimization. - SHMEM, data-parallel DP scheduling, and DeepSeek-V3 integration. - Code quality and test discipline through targeted test improvements and reliability fixes.
13 Commits • 1 Features
Oct 1, 2025October 2025—modularml/mojo: Delivering scalable Expert Parallelism (EP) for distributed MoE/inference, strengthening performance, reliability, and model coverage across our platform. Key features delivered: - End-to-end EP for distributed MoE/inference: initialization infra, dispatch and combine pipelines, MoE sharding, token format abstractions with FP8 quantization, and integration with DeepSeek-V3; enabling direct device-to-device communication and optimized EP kernel paths across distributed GPUs. - EP integration across core modules: EP support added to the MoE SDK/module, DeepSeek-V3 model compatibility, and FP8-based token encoding through TokenFormat traits; introduced BlockwiseFP8TokenFormat for ep_dispatch kernels. - Kernel and execution flow improvements: wired up EP dispatch and combine kernels and completed the EPBatchManager with combine-related functionality to streamline end-to-end EP execution. Major bugs fixed: - Stabilized SHMEM tests and ensured correctness across test suites; robustified grouped_matmul tests; fixed static stride usage in the Gumbel kernel; improved per-device chunked request handling in data-parallel text generation. - Additional test improvements and bug fixes aimed at improving scheduling accuracy and overall runtime robustness. Overall impact and accomplishments: - Significantly improved scalability and throughput for distributed MoE workloads; introduced a robust EP-enabled path with better cross-GPU communication, reduced test flakiness, and enhanced scheduling stability for DP workloads. - Expanded model coverage with EP-enabled DeepSeek-V3 integration, broadening applicability of the EP workflow across production models. Technologies/skills demonstrated: - Distributed systems, MoE architecture, FP8 quantization, token format abstractions, and kernel-level EP optimization. - SHMEM, data-parallel DP scheduling, and DeepSeek-V3 integration. - Code quality and test discipline through targeted test improvements and reliability fixes.
October 2025
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 — Key distributed training enhancements and stability improvements for modularml/mojo. Delivered Expert Parallelism (EP) communications in SHMEM to enable efficient routing across ranks, introduced non-blocking SHMEM API in the SDK for asynchronous, overlap-able operations, and fixed correctness for distributed matrix multiplication with uneven partitions. These changes improve scalability, reduce synchronization bottlenecks, and improve reliability in production workloads.
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 — Key distributed training enhancements and stability improvements for modularml/mojo. Delivered Expert Parallelism (EP) communications in SHMEM to enable efficient routing across ranks, introduced non-blocking SHMEM API in the SDK for asynchronous, overlap-able operations, and fixed correctness for distributed matrix multiplication with uneven partitions. These changes improve scalability, reduce synchronization bottlenecks, and improve reliability in production workloads.
August 2025
5 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for modularml/mojo: Delivered major features enabling non-uniform models via subgraph layer groups in DistributedTransformer, added 3D RoPE support in fused_qk_rope_ragged, improved SM90 grouped matmul dispatch for BF16 performance, and fixed FP8 matmul correctness with updated scaling and tests. These efforts expand model capabilities, boost performance, and strengthen test coverage, delivering tangible business value in throughput, accuracy, and hardware efficiency.
5 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for modularml/mojo: Delivered major features enabling non-uniform models via subgraph layer groups in DistributedTransformer, added 3D RoPE support in fused_qk_rope_ragged, improved SM90 grouped matmul dispatch for BF16 performance, and fixed FP8 matmul correctness with updated scaling and tests. These efforts expand model capabilities, boost performance, and strengthen test coverage, delivering tangible business value in throughput, accuracy, and hardware efficiency.
August 2025
July 2025
13 Commits • 2 Features
Jul 1, 2025Overview for 2025-07: Delivered a generalized Mixture of Experts (MoE) framework and integrated it with Deepseek-V2-lite and Llama4, centralized MoE logic, and added SafeTensors handle caching for stability, enabling easier model experimentation and scalable routing. Implemented distributed tensor parallelism across MLA, MoE, and DeepSeek-V2, including Distributed Latent Attention with Rope for MLA, tensor-parallel MoE, and multi-device support for DeepSeek-V2, unlocking multi-device training and inference scalability. Aligned RMSNorm precision across Deepseek and Llama4 and fixed dtype handling in fused_qk_ragged_rope, improving numerical stability and reference-consistent behavior. Improved penalties and kernels: corrected token sampler penalty application order, enhanced H100 epilogue handling, and passed static shape information to batched matmul, boosting throughput. Removed outdated weight adapter workaround by passing expert weights directly to the graph compiler, simplifying the pipeline and improving maintainability. This work collectively improves scalability, stability, and performance, enabling broader deployment of large MoE models and faster iteration for model experimentation.
July 2025
13 Commits • 2 Features
Jul 1, 2025Overview for 2025-07: Delivered a generalized Mixture of Experts (MoE) framework and integrated it with Deepseek-V2-lite and Llama4, centralized MoE logic, and added SafeTensors handle caching for stability, enabling easier model experimentation and scalable routing. Implemented distributed tensor parallelism across MLA, MoE, and DeepSeek-V2, including Distributed Latent Attention with Rope for MLA, tensor-parallel MoE, and multi-device support for DeepSeek-V2, unlocking multi-device training and inference scalability. Aligned RMSNorm precision across Deepseek and Llama4 and fixed dtype handling in fused_qk_ragged_rope, improving numerical stability and reference-consistent behavior. Improved penalties and kernels: corrected token sampler penalty application order, enhanced H100 epilogue handling, and passed static shape information to batched matmul, boosting throughput. Removed outdated weight adapter workaround by passing expert weights directly to the graph compiler, simplifying the pipeline and improving maintainability. This work collectively improves scalability, stability, and performance, enabling broader deployment of large MoE models and faster iteration for model experimentation.
June 2025
22 Commits • 12 Features
Jun 1, 2025June 2025 monthly summary for modularml/mojo. Focused on delivering high-impact features, stabilizing core kernels, and boosting performance and memory efficiency to drive scalability and reliability in production workloads. Highlights include nucleus (top-p) sampling integration in Pipelines/Kernels, padded attention support for the flash attention GPU kernel, and per-token normalization in KVCache. Significant GPU performance and memory improvements were achieved via ops.fold GPU optimization, preallocated cuFFT buffers, and reduced memory footprint in ops.irfft. Exposed max_pool and avg_pool in the graph API and wired their GPU implementations, enabling broader model architectures. Implemented multiple correctness fixes across kernels and pipelines including MHA SM90 prefill masking, group_norm Welford participation, SIMD alignment, token frequency computation, RNG seed management, repetition penalty, and PyTorch DLPack/pinned memory workaround.
22 Commits • 12 Features
Jun 1, 2025June 2025 monthly summary for modularml/mojo. Focused on delivering high-impact features, stabilizing core kernels, and boosting performance and memory efficiency to drive scalability and reliability in production workloads. Highlights include nucleus (top-p) sampling integration in Pipelines/Kernels, padded attention support for the flash attention GPU kernel, and per-token normalization in KVCache. Significant GPU performance and memory improvements were achieved via ops.fold GPU optimization, preallocated cuFFT buffers, and reduced memory footprint in ops.irfft. Exposed max_pool and avg_pool in the graph API and wired their GPU implementations, enabling broader model architectures. Implemented multiple correctness fixes across kernels and pipelines including MHA SM90 prefill masking, group_norm Welford participation, SIMD alignment, token frequency computation, RNG seed management, repetition penalty, and PyTorch DLPack/pinned memory workaround.
June 2025
May 2025
23 Commits • 18 Features
May 1, 2025May 2025 (2025-05) was a feature-rich sprint for modularml/mojo, delivering expanded FP8/float8 support across Llama models, significant end-to-end and pipeline enhancements, and targeted kernel/GPU optimizations. The work focused on enabling lower-precision inference for large models, improving sampling quality and control, and expanding model and pipeline capabilities to support broader use cases and business value.
May 2025
23 Commits • 18 Features
May 1, 2025May 2025 (2025-05) was a feature-rich sprint for modularml/mojo, delivering expanded FP8/float8 support across Llama models, significant end-to-end and pipeline enhancements, and targeted kernel/GPU optimizations. The work focused on enabling lower-precision inference for large models, improving sampling quality and control, and expanding model and pipeline capabilities to support broader use cases and business value.
April 2025
38 Commits • 17 Features
Apr 1, 2025April 2025 monthly summary for modularml/mojo: Delivered a comprehensive MLA/MoE kernel suite, extended sequence handling, and reliability improvements across the pipeline, enabling higher throughput and more scalable inference/training workloads. Core focus on end-to-end performance, memory efficiency, and stability for large-model workloads. Highlights include developing an MLA prefill kernel and planning kernels (with support for merging previous attention results in MLA prefill), adding a K-cache decompression kernel, extending LatentAttentionWithRope to support max_seq_len > 1, introducing MoE indices calculation kernel with tensor parallelism for MoE layers in llama4, and enhancing SlidingWindowMask capabilities with a dedicated kernel, interface wiring, and 64-bit instruction checks. Additional improvements covered zero-filling for MLA workload planning and GPU memory transfer support.
38 Commits • 17 Features
Apr 1, 2025April 2025 monthly summary for modularml/mojo: Delivered a comprehensive MLA/MoE kernel suite, extended sequence handling, and reliability improvements across the pipeline, enabling higher throughput and more scalable inference/training workloads. Core focus on end-to-end performance, memory efficiency, and stability for large-model workloads. Highlights include developing an MLA prefill kernel and planning kernels (with support for merging previous attention results in MLA prefill), adding a K-cache decompression kernel, extending LatentAttentionWithRope to support max_seq_len > 1, introducing MoE indices calculation kernel with tensor parallelism for MoE layers in llama4, and enhancing SlidingWindowMask capabilities with a dedicated kernel, interface wiring, and 64-bit instruction checks. Additional improvements covered zero-filling for MLA workload planning and GPU memory transfer support.
April 2025
March 2025
17 Commits • 4 Features
Mar 1, 2025Concise monthly summary for modularml/mojo focusing on business value and technical accomplishments in March 2025. Delivered targeted kernel and KV-cache enhancements, improving throughput, stability, and support for ragged/paged inputs across attention paths.
March 2025
17 Commits • 4 Features
Mar 1, 2025Concise monthly summary for modularml/mojo focusing on business value and technical accomplishments in March 2025. Delivered targeted kernel and KV-cache enhancements, improving throughput, stability, and support for ragged/paged inputs across attention paths.
Activity
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Quality Metrics
Correctness90.6%
Maintainability84.4%
Architecture87.6%
Performance84.6%
AI Usage20.2%
Skills & Technologies
Programming Languages
BazelMojoPythonType HintingYAML
Technical Skills
AI AccelerationAI Model OptimizationAI model optimizationAPI DesignAPI DevelopmentAPI developmentAlgorithm OptimizationAssembly Language AnalysisAsynchronous ProgrammingAttention MechanismsAttention mechanismsBackend DevelopmentBatch ProcessingBazel Build SystemC++ (implied via BUILD.bazel)
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