tzhouamandClaude Opus 5
3834de3105
Renamed from vllm-embedding: nothing here is about embeddings, and the name had come from the parent working directory. edge-infer says what it does. The GitHub repository is renamed to match; nothing had been pushed yet. The audit found the packaging was incomplete in two ways. Scripts: 56 of them produced results that are published in this repo but were not included, so the numbers could be read and not checked. Added under benchmarks/edge_harness/experiments/ (one-off drivers: the AI Hub on-device runs, the 4-bit GEMM microbenchmark, fidelity and parity checks, checkpoint build and round-trip, activation capture, and the load generator used to prove the contention guard actually refuses) and benchmarks/edge_harness/runs/ (the 24 shell drivers behind published grids). Both directories carry a README saying plainly that they are a record of what was run rather than a supported interface -- most hardcode absolute paths from the machine they ran on. The supported entry points remain the tools one level up. One broken relative link fixed (comparison.md is under docs/engines/, not docs/analysis/). All other links and every script reference in the docs now resolve inside the repo. Secret scan: the token in ~/.qai_hub/client.ini is absent from the worktree and from all 61 commits. Worth noting that an earlier version of my own check reported a false positive, because `grep -rl ... | head` takes its exit status from head, which always succeeds -- the same class of mistake as the measurement errors this project keeps recording. The AI Hub scripts authenticate by reading that file at runtime and contain no credential. README reframed for the new name and the wider scope: the repository is the engine changes, the harness, the raw results, the baselines and the analyses, not just a patch set. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
"""Numerical parity: HF reference Spark-X2.5 vs the vLLM-Omni implementation.
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Runs both on CPU, greedy, and compares generated ids plus the first-step
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logit vector.
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"""
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import argparse, json, os, sys
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import numpy as np
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import torch
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MODEL = os.environ.get("SPARK_MODEL", "XHToken/Spark-X2.5-1.7B")
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PROMPT_MSGS = [
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{"role": "user", "content": "What is the capital of France? Answer in one word."},
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]
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def hf_reference(model_path, prompt_ids, n_new, dtype):
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from transformers import AutoConfig, AutoModelForCausalLM
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from transformers.dynamic_module_utils import get_class_from_dynamic_module
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# modeling_spark.py targets transformers 4.57; v5 expects the tied-weight
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# map to be a dict rather than a list of keys.
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cfg = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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cls = get_class_from_dynamic_module(
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cfg.auto_map["AutoModelForCausalLM"], model_path
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)
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if isinstance(getattr(cls, "_tied_weights_keys", None), list):
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cls._tied_weights_keys = {"lm_head.weight": "model.embedding.weight"}
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m = cls.from_pretrained(model_path, dtype=dtype).eval()
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ids = torch.tensor([prompt_ids])
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with torch.no_grad():
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out = m(ids)
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first_logits = out.logits[0, -1].float().numpy()
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gen = m.generate(ids, max_new_tokens=n_new, do_sample=False,
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pad_token_id=2)
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return first_logits, gen[0, len(prompt_ids):].tolist()
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--n-new", type=int, default=24)
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ap.add_argument("--dtype", default="bfloat16")
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ap.add_argument("--max-model-len", type=int, default=2048)
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ap.add_argument("--out", default="parity.json")
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args = ap.parse_args()
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dtype = getattr(torch, args.dtype)
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from transformers import AutoTokenizer
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tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
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prompt = tok.apply_chat_template(PROMPT_MSGS, tokenize=False,
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add_generation_prompt=True)
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prompt_ids = tok(prompt, add_special_tokens=False)["input_ids"]
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print(f"prompt tokens: {len(prompt_ids)}")
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hf_logits, hf_ids = hf_reference(MODEL, prompt_ids, args.n_new, dtype)
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print("HF :", repr(tok.decode(hf_ids)))
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import vllm_omni # noqa: F401 (registers Spark2_5ForCausalLM)
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from vllm import LLM, SamplingParams
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llm = LLM(model=MODEL, dtype=args.dtype, max_model_len=args.max_model_len,
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enforce_eager=True, gpu_memory_utilization=0.30,
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max_num_seqs=1, trust_remote_code=False)
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sp = SamplingParams(temperature=0.0, max_tokens=args.n_new, logprobs=0,
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prompt_logprobs=0)
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res = llm.generate([{"prompt_token_ids": prompt_ids}], sp)[0]
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v_ids = list(res.outputs[0].token_ids)
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print("vLLM:", repr(res.outputs[0].text))
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n = min(len(hf_ids), len(v_ids))
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match = sum(int(a == b) for a, b in zip(hf_ids[:n], v_ids[:n]))
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# first generated token's logit agreement
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first_hf_top = int(np.argmax(hf_logits))
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report = {
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"prompt_len": len(prompt_ids),
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"dtype": args.dtype,
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"hf_text": tok.decode(hf_ids),
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"vllm_text": res.outputs[0].text,
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"hf_ids": hf_ids, "vllm_ids": v_ids,
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"greedy_token_match": f"{match}/{n}",
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"exact_match": hf_ids[:n] == v_ids[:n],
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"hf_first_top1": first_hf_top,
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"vllm_first_token": v_ids[0] if v_ids else None,
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"first_token_agree": bool(v_ids and v_ids[0] == first_hf_top),
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}
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json.dump(report, open(args.out, "w"), indent=2)
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print(json.dumps({k: v for k, v in report.items()
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if k not in ("hf_ids", "vllm_ids")}, indent=2))
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return 0 if report["exact_match"] else 1
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if __name__ == "__main__":
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sys.exit(main())
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