wuyang
57 lines
1.8 KiB
Markdown
57 lines
1.8 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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