Claude/debug huggingface 404 dq g ca

run_cacheslide.sh

@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+# Execution commands for CacheSlide example.
+# Run from the repo root: bash run_cacheslide.sh [hotpotqa|msc|swebench]
+set -euo pipefail
+
+cd "$(dirname "$0")"
+
+# 1) Syntax-check the two patched files (fast smoke test).
+python - <<'PY'
+import ast
+for f in [
+    "vllm/model_executor/models/llama.py",
+    "vllm/worker/model_runner.py",
+]:
+    ast.parse(open(f).read())
+    print(f"parse OK: {f}")
+
+# Quick sanity check: CoPEPositionalEncoder behaves on its own.
+import importlib.util, pathlib, sys, torch
+spec = importlib.util.spec_from_file_location(
+    "_llama_check", "vllm/model_executor/models/llama.py"
+)
+# We can't import the module fully (depends on vllm runtime), so just
+# eval the class block in isolation by extracting it.
+src = pathlib.Path("vllm/model_executor/models/llama.py").read_text()
+start = src.index("class CoPEPositionalEncoder")
+end = src.index("\nclass ", start + 1)
+ns = {"torch": torch, "nn": torch.nn, "Tuple": tuple}
+exec("import torch.nn as nn\n" + src[start:end], ns)
+CoPE_PE = ns["CoPEPositionalEncoder"]
+enc = CoPE_PE(fixed_ranges=[(0, 8), (8, 20)])
+assert enc.get_range(0) == (0, 8) and enc.get_range(1) == (8, 20)
+assert enc.assign(2, 5) == (20, 25)
+out = enc.apply(torch.zeros(4, dtype=torch.long), chunk_id=1)
+assert out.tolist() == [8, 9, 10, 11], out.tolist()
+print("CoPEPositionalEncoder smoke test OK")
+PY
+
+# 2) Pick dataset preset (default: hotpotqa).
+DATASET="${1:-hotpotqa}"
+
+# 3) Run the harness.
+#    --gpu_mem_util may need lowering on smaller GPUs.
+python examples/CacheSlide.py \
+    --model mistralai/Mistral-7B-Instruct-v0.2 \
+    --gpu_mem_util 0.5 \
+    --dataset "${DATASET}" \
+    --split validation \
+    --num_samples 5 \
+    --max_chunks 4 \
+    --max_new_tokens 16 \
+    --start_index 0 \
+    --seed 0
+
+# Per-dataset variants:
+# bash run_cacheslide.sh hotpotqa
+# bash run_cacheslide.sh msc
+# bash run_cacheslide.sh swebench