Repository Analysis

deepseek-ai/DeepEP

DeepEP: an efficient expert-parallel communication library

5.6 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of deepseek-ai/DeepEP, a Cuda project with 9,844 GitHub stars. SynthScan v2.0 examined 13,039 lines of code across 67 source files, recording 67 pattern matches distributed across 10 syntactic categories. The overall adjusted score of 5.6 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

5.6
Adjusted Score
5.6
Raw Score
100%
Time Factor
2026-07-14
Last Push
9.8K
Stars
Cuda
Language
13.0K
Lines of Code
67
Files
67
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 2LOW 65

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 67 distinct pattern matches across 10 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers19 hits · 20 pts
SeverityFileLineSnippetContext
LOWsetup.py42def get_nvshmem_host_lib_name(base_dir):CODE
LOWdeep_ep/buffers/legacy.py176 def get_low_latency_rdma_size_hint(num_max_dispatch_tokens_per_rank: int, hidden: int, num_ranks: int, num_experts: CODE
LOWdeep_ep/buffers/legacy.py672 def low_latency_update_mask_buffer(self, rank_to_mask: int, mask: bool = False):CODE
LOWdeep_ep/buffers/legacy.py683 def low_latency_query_mask_buffer(self, mask_status: torch.Tensor):CODE
LOWdeep_ep/buffers/legacy.py693 def low_latency_clean_mask_buffer(self):CODE
LOWdeep_ep/buffers/legacy.py700 def get_next_low_latency_combine_buffer(self, handle: object):CODE
LOWdeep_ep/buffers/elastic.py409 def get_engram_storage_size_hint(num_entries: int, hidden: int,CODE
LOWdeep_ep/buffers/elastic.py459 def get_agrs_num_max_session_bytes(group: dist.ProcessGroup,CODE
LOWdeep_ep/buffers/elastic.py480 def get_agrs_buffer_size_hint(group: dist.ProcessGroup,CODE
LOWdeep_ep/utils/envs.py183def check_torch_deterministic() -> None:CODE
LOWdeep_ep/utils/envs.py223def check_fast_rdma_atomic_support(nic_name: str = _DEFAULT_NIC_NAME) -> bool:CODE
LOWdeep_ep/utils/comm.py78def destroy_all_managed_nccl_comm() -> None:CODE
LOWdeep_ep/utils/gate.py116def get_precise_unbalanced_scores(num_tokens: int, num_experts: int, num_ranks: int, num_topk: int, ratio: float):CODE
LOWdeep_ep/utils/gate.py148def map_unbalanced_ratio_to_factor(num_tokens: int, num_experts: int, num_ranks: int, num_topk: int, ratio: float) -> flCODE
LOWdeep_ep/utils/gate.py167def get_random_unbalanced_scores(num_tokens: int, num_experts: int, num_ranks: int, num_topk: int, ratio: float):CODE
LOWdeep_ep/utils/refs.py126def generate_pre_combine_data(src_token_global_idx: torch.Tensor,CODE
LOWtests/elastic/test_ep.py295 def get_unique_and_valid_dst_count(dst_idx: torch.Tensor,CODE
LOWtests/legacy/test_low_latency.py14def simulate_failure_and_skip(rank: int, api: Literal["dispatch", "combine", "clean"], expected_masked_ranks: Set[int]):CODE
LOWtests/legacy/test_low_latency.py33def query_mask_buffer_and_check(api: Literal["dispatch", "combine", "clean"], buffer: deep_ep.Buffer, mask_status: torchCODE
Unused Imports13 hits · 13 pts
SeverityFileLineSnippetContext
LOWdeep_ep/__init__.py5CODE
LOWdeep_ep/__init__.py88CODE
LOWdeep_ep/__init__.py89CODE
LOWdeep_ep/__init__.py89CODE
LOWdeep_ep/__init__.py91CODE
LOWdeep_ep/__init__.py91CODE
LOWdeep_ep/__init__.py92CODE
LOWdeep_ep/__init__.py92CODE
LOWdeep_ep/__init__.py95CODE
LOWdeep_ep/__init__.py95CODE
LOWdeep_ep/buffers/elastic.py6CODE
LOWdeep_ep/utils/__init__.py3CODE
LOWtests/elastic/test_agrs.py2CODE
Deep Nesting11 hits · 11 pts
SeverityFileLineSnippetContext
LOWdeep_ep/utils/envs.py145CODE
LOWdeep_ep/utils/testing.py111CODE
LOWdeep_ep/utils/find_pkgs.py8CODE
LOWtests/elastic/test_ep.py22CODE
LOWtests/elastic/test_ep.py59CODE
LOWtests/elastic/test_agrs.py89CODE
LOWtests/legacy/test_low_latency.py39CODE
LOWtests/legacy/test_intranode.py17CODE
LOWtests/legacy/test_internode.py18CODE
LOWtests/legacy/test_internode.py318CODE
LOWtests/utils/test_gate.py7CODE
Over-Commented Block8 hits · 8 pts
SeverityFileLineSnippetContext
LOWformat.sh1#!/usr/bin/env bashCOMMENT
LOWcsrc/python_api.cpp1#include <pybind11/pybind11.h>COMMENT
LOWcsrc/kernels/elastic/api.hpp1#pragma onceCOMMENT
LOWcsrc/elastic/buffer.hpp1#pragma onceCOMMENT
LOWcsrc/legacy/buffer.hpp1#pragma onceCOMMENT
LOWcsrc/utils/system.hpp1#pragma onceCOMMENT
LOWcsrc/utils/format.hpp1#pragma onceCOMMENT
LOWcsrc/jit/compiler.hpp1#pragma onceCOMMENT
AI Structural Patterns7 hits · 7 pts
SeverityFileLineSnippetContext
LOWdeep_ep/buffers/legacy.py33CODE
LOWdeep_ep/buffers/legacy.py322CODE
LOWdeep_ep/buffers/legacy.py458CODE
LOWdeep_ep/buffers/elastic.py228CODE
LOWdeep_ep/buffers/elastic.py855CODE
LOWdeep_ep/buffers/elastic.py1046CODE
LOWdeep_ep/utils/refs.py243CODE
Self-Referential Comments2 hits · 6 pts
SeverityFileLineSnippetContext
MEDIUMdeep_ep/utils/comm.py71 # Create a new communicatorCOMMENT
MEDIUMdeep_ep/utils/gate.py20 # Create the maskCOMMENT
Excessive Try-Catch Wrapping4 hits · 4 pts
SeverityFileLineSnippetContext
LOWdeep_ep/__init__.py38 except Exception:CODE
LOWdeep_ep/utils/envs.py217 except Exception as e:CODE
LOWdeep_ep/utils/envs.py241 except Exception:CODE
LOWdeep_ep/utils/envs.py266 except Exception as e:CODE
Example Usage Blocks1 hit · 2 pts
SeverityFileLineSnippetContext
LOWformat.sh2# Usage:COMMENT
Redundant / Tautological Comments1 hit · 2 pts
SeverityFileLineSnippetContext
LOWformat.sh179# Check if there are any uncommitted changes after all formatting steps.COMMENT
Modern Structural Boilerplate1 hit · 1 pts
SeverityFileLineSnippetContext
LOWdeep_ep/buffers/legacy.py154 def set_num_sms(new_num_sms: int) -> None:CODE