WpyQwq
643e22ecb9
- 引入 MemoryRouterXL 与 v5/v6 流式多线程训练/编码管线 - 修复 prepare_memory_router_dataset 候选池重建缺陷（mega 家族 3568x 加速，输出逐字节相同） - 修复 v5 被破坏的拒答与多跳标签（train 未知样本 319 -> 16319，multi_hop 平均正例 1.00 -> 2.00） - 同存储预算下 V2-128 v6 逐轴 22/22 通过：Top-1 41.12% -> 94.62%，未知拒答 0.00% -> 100.00% - 记录三条被实测推翻的显然优化（logits_to_keep=1 反而慢 55%、XL 容量未带来收益） - 记忆手术跨架构可移植性 14/14，读写关闭时与原生模型逐位相同
31 lines
1.3 KiB
Python
31 lines
1.3 KiB
Python
"""Fast environment and gradient smoke test."""
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from __future__ import annotations
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import torch
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from .model import DynamicMemoryConfig, DynamicMemoryLM, count_parameters
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from .tasks import sample_associative_batch
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def main() -> None:
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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config = DynamicMemoryConfig(vocab_size=64, max_seq_len=16, d_model=64, n_layers=2, n_heads=4, memory_slots=4)
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model = DynamicMemoryLM(config).to(device)
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batch = sample_associative_batch(batch_size=8, vocab_size=config.vocab_size, device=device)
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memory = model(batch.learn_chunks[0]).memory
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output = model(batch.query_input, memory=memory, update_memory=False, labels=batch.query_labels)
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if output.loss is None or not torch.isfinite(output.loss):
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raise RuntimeError("non-finite loss")
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output.loss.backward()
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gradients = [p.grad for p in model.parameters() if p.grad is not None]
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if not gradients:
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raise RuntimeError("no gradients produced")
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print(f"smoke_ok device={device} parameters={count_parameters(model):,} loss={output.loss.detach().item():.4f}")
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if device.type == "cuda":
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print(f"gpu={torch.cuda.get_device_name(0)} memory_allocated_mb={torch.cuda.memory_allocated() / 1024**2:.1f}")
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if __name__ == "__main__":
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main()
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