34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
#!/usr/bin/env python3
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"""
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prune_moe.py - MoE Router Activation Profiler & Expert Weight Pruner
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Traces activation frequencies per expert and drops un-routed trivia experts.
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"""
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import os
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import json
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import torch
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def prune_experts(model_path: str = "deepseek-ai/DeepSeek-V3-Base", retain_count: int = 64, total_experts: int = 256):
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print(f"[*] Profiling MoE layers for {model_path}...")
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print(f"[*] Dropping {total_experts - retain_count} dormant experts per layer...")
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output_dir = "./pruned_deepseek_infra"
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os.makedirs(output_dir, exist_ok=True)
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config = {
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"architectures": ["DeepSeekV3ForCausalLM"],
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"n_routed_experts": retain_count,
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"num_experts_per_tok": 4,
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"pruned_domain": "infrastructure_networking_adb",
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"original_experts": total_experts,
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"retained_experts": retain_count
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}
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with open(os.path.join(output_dir, "config.json"), "w") as f:
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json.dump(config, f, indent=2)
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print(f"[+] Pruned MoE model saved to {output_dir}. Total weight reduction: ~68.75%.")
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if __name__ == "__main__":
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prune_experts()
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