wuyang
103 lines
3.5 KiB
Markdown
103 lines
3.5 KiB
Markdown
# DeepSeek real-weight execution probes
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The probes in this directory use official DeepSeek artifacts and keep their
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scope deliberately narrower than a full-model benchmark.
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## DeepSeek-V2-Lite truncated trace
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`v2_lite_trace.py` executes layers 0–6 from the official BF16 checkpoint. Those
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seven layers are fully contained in shard 1; layer 7 is split across shards 1
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and 2 and is therefore outside the default evidence boundary.
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Pinned model:
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```text
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deepseek-ai/DeepSeek-V2-Lite@604d5664dddd88a0433dbae533b7fe9472482de0
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```
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Required Python stack:
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```text
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torch==2.11.0+cu128
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transformers==4.41.2
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safetensors==0.8.0
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```
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The 2024 remote code does not import under Transformers 5.5 because
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`is_torch_fx_available` was removed. The probe imports the official files as a
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read-only local package; it does not patch the model source.
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Download the metadata, tokenizer, remote code, index, and first shard with the
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Hugging Face CLI, then run:
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```bash
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python experiments/deepseek/v2_lite_trace.py \
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--artifact-dir /path/to/deepseek-v2-lite \
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--output src/data/deepseek-v2-lite-trace.json
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```
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The result contains:
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- real tokenizer pieces and model-derived hidden states;
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- the actual `[B,T,576]` MLA compressed projection at each executed layer;
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- the expanded key/value tensors stored by the Hugging Face eager cache;
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- token-level top-6 routed expert IDs and weights for six MoE layers;
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- per-layer and per-prompt expert-load summaries;
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- explicit boundaries against global load, expert semantics, training traces,
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full-model generation, and production serving claims.
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Run the probe twice and compare deterministic evidence while excluding timing:
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```bash
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python experiments/deepseek/compare_v2_lite_traces.py \
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--first /path/to/trace-1.json \
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--second /path/to/trace-2.json \
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--output src/data/deepseek-v2-lite-trace-repro.json
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```
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## Fixed public routing corpus
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`v2_lite_routing_corpus.py` keeps the same official layer 0–6 execution boundary
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but replaces the four authored prompts with 128 source-addressable public
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prompts:
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- 32 WikiText-2 raw validation passages;
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- 32 CLUE TNEWS public-test sentences;
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- 32 OpenAI HumanEval prompts, without solutions/tests or code execution;
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- 32 OpenAI GSM8K test questions, without answers.
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Selection is the ascending SHA-256 rank of a fixed salt, domain, and source ID.
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Inputs are truncated to 96 DeepSeek tokens. The six MoE layers therefore produce
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304,560 actual top-6 routed-expert selections over 8,460 valid tokens.
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The output includes both token-weighted and prompt-balanced distributions. Its
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95% intervals use 2,000 prompt-level bootstrap resamples within each domain,
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rather than treating correlated tokens as independent observations.
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```bash
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PYTHONPATH=/path/to/transformers-4.41.2-deps \
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python -B experiments/deepseek/v2_lite_routing_corpus.py \
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--artifact-dir /path/to/deepseek-v2-lite \
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--human-eval /path/to/HumanEval.jsonl.gz \
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--gsm8k /path/to/gsm8k/test.jsonl \
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--tnews /path/to/tnews/test.json \
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--tnews-archive /path/to/tnews_public.zip \
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--wikitext /path/to/wikitext-validation.parquet \
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--output src/data/deepseek-v2-lite-routing-corpus.json \
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--per-domain 32 \
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--max-tokens 96 \
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--batch-size 16 \
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--bootstrap 2000 \
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--seed 20260729 \
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--captured-at 2026-07-29T07:45:00+00:00
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```
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The committed independent rerun is byte-exact. Both JSON files have SHA-256:
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```text
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4678a1d15395de93ffba757598cc3642bf35e9e07f71d82ddd87c27fc38a09e4
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```
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See `research/DEEPSEEK_ROUTING_CORPUS_AUDIT.md` for corpus revisions and hashes,
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metric definitions, interval semantics, results, and claim boundaries.
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