Updated key benchmark findings
This commit is contained in:
Donato Capitella
@@ -1,118 +1,174 @@
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#!/usr/bin/env python3
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import json
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import json, re
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from collections import defaultdict
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from pathlib import Path
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# --- Config ---
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RESULTS_JSON = Path("../docs/results.json")
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RESULTS_FILE = "../docs/results.json"
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# Column order + labels
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ENV_ORDER = [
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"vulkan_amdvlk",
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"vulkan_radv",
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"rocm6_4_2",
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"rocm6_4_2-rocwmma",
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"rocm7_beta",
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"rocm7_rc"
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"rocm7_rc",
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]
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COL_NAMES = {
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"vulkan_amdvlk": "Vulkan (AMDVLK)",
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"vulkan_radv": "Vulkan (RADV)",
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"rocm6_4_2": "ROCm 6.4.2",
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"rocm6_4_2-rocwmma": "ROCm 6.4.2 + ROCWMMA",
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"rocm7_beta": "ROCm 7.0 Beta",
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"rocm7_rc": "ROCm 7.0 RC"
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"rocm7_rc": "ROCm 7.0 RC",
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}
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WINNER_LABELS = {
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WINNER_NAMES = {
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"vulkan_amdvlk": "AMDVLK",
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"vulkan_radv": "RADV",
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"rocm6_4_2": "ROCm6.4.2",
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"rocm6_4_2-rocwmma": "ROCm6.4.2+ROCWMMA",
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"rocm7_beta": "ROCm7 Beta",
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"rocm7_rc": "ROCm7 RC"
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"rocm7_rc": "ROCm7 RC",
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}
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DEFAULT_MODELS = [
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("Gemma3 12B Q8_0", "gemma-3-12b-it-UD-Q8_K_XL"),
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("Gemma3 27B BF16", "gemma-3-27b-it-BF16"),
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("Llama-4-Scout 17B Q8_0", "Llama-4-Scout-17B-16E-Instruct-Q8_0"),
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("Llama-4-Scout 17B Q4_K XL", "Llama-4-Scout-17B-16E-Instruct-UD-Q4_K_XL"),
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("Qwen3 30B BF16", "Qwen3-30B-A3B-BF16"),
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("Qwen3-235B Q3_K XL", "Qwen3-235B-A22B-Instruct-2507-UD-Q3_K_XL"),
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("GLM-4.5-Air-Q4_K_XL", "GLM-4.5-Air-UD-Q4_K_XL"),
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("GLM-4.5-Air-Q6_K_XL", "GLM-4.5-Air-UD-Q6_K_XL"),
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("gpt-oss-120b-mxfp4", "gpt-oss-120b-mxfp4"),
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("gpt-oss-20b-mxfp4", "gpt-oss-20b-mxfp4"),
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]
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ERROR_LABELS = {
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ERROR_LABEL = {
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"load": "⚠️ Load Error",
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"hang": "⚠️ GPU Hang",
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"runtime": "⚠️ Runtime Error"
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"runtime": "⚠️ Runtime Error",
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}
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# --- Helpers ---
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def load_results():
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data = json.loads(Path(RESULTS_JSON).read_text())
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return data["runs"]
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# Display name → fuzzy key (case/UD/shard-insensitive)
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DEFAULT_MODELS = [
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("Gemma3 12B Q8_0", "gemma-3-12b"),
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("Gemma3 27B BF16", "gemma-3-27b"),
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("Llama-4-Scout 17B Q8_0", "llama-4-scout-17b-16e-instruct-q8_0"),
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("Llama-4-Scout 17B Q4_K XL", "llama-4-scout-17b-16e-instruct-q4_k_xl"),
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("Qwen3 30B BF16", "qwen3-30b-a3b-bf16"),
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("Qwen3-235B Q3_K XL", "qwen3-235b-a22b"),
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("GLM-4.5-Air-Q4_K_XL", "glm-4.5-air-q4_k_xl"),
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("GLM-4.5-Air-Q6_K_XL", "glm-4.5-air-q6_k_xl"),
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("gpt-oss-120b-mxfp4", "gpt-oss-120b-mxfp4"),
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("gpt-oss-20b-mxfp4", "gpt-oss-20b-mxfp4"),
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]
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def filter_runs(runs, model_prefix, env):
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for r in runs:
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if r["model_clean"].startswith(model_prefix) and r["env"] == env:
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return r
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SHARD_RE = re.compile(r"-000\d+-of-000\d+", re.IGNORECASE)
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def norm_model(s: str) -> str:
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s = (s or "").lower().replace("_", "-")
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s = SHARD_RE.sub("", s)
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s = s.replace("-ud", "") # drop -UD tag for matching
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return s
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# Load JSON
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raw = json.loads(Path(RESULTS_FILE).read_text(encoding="utf-8"))
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runs = raw["runs"]
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# Bucket rows by (model_key, env, test, fa)
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buckets = defaultdict(list)
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error_only = defaultdict(list) # (model_key, env) -> [error_type,...] for test=None rows
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all_models = set()
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for r in runs:
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env = r.get("env")
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if env not in ENV_ORDER:
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continue
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mkey = norm_model(r.get("model_clean") or r.get("model") or "")
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all_models.add(mkey)
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test = r.get("test") # "pp512", "tg128", or None for pure errors
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if test in ("pp512", "tg128"):
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buckets[(mkey, env, test)].append(r)
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else:
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# capture error-only rows so we can show ⚠️ instead of "—"
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if r.get("error"):
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error_only[(mkey, env)].append(r.get("error_type") or "runtime")
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def pick_best(rows):
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"""Choose the best non-error row by tps_mean; if all error, return an error row."""
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best = None
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best_val = -1
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fallback = None
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for r in rows:
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if r.get("error"):
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fallback = r
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continue
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v = r.get("tps_mean")
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if isinstance(v, (int, float)) and v > best_val:
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best_val = v
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best = r
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return best or fallback
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# Build chosen results per (model, env): {pp: row|None, tg: row|None, err_only: str|None}
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chosen = defaultdict(lambda: defaultdict(dict))
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for (mkey, env, test), rows in buckets.items():
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chosen_row = pick_best(rows)
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chosen[mkey][env][test] = chosen_row
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for (mkey, env), etypes in error_only.items():
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if etypes:
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# prefer specific types in a stable order
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if "load" in etypes:
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chosen[mkey][env]["error_only"] = "load"
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elif "hang" in etypes:
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chosen[mkey][env]["error_only"] = "hang"
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else:
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chosen[mkey][env]["error_only"] = "runtime"
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def format_cell(entry_dict):
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pp = entry_dict.get("pp512")
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tg = entry_dict.get("tg128")
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# If either chosen row is an error, show that error (web UI behavior)
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for row in (pp, tg):
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if row and row.get("error"):
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return ERROR_LABEL.get(row.get("error_type") or "runtime", "⚠️ Error")
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# If both pp/tg missing but we have an error-only marker, show it
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if not pp and not tg:
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et = entry_dict.get("error_only")
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if et:
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return ERROR_LABEL.get(et, "⚠️ Error")
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return "—" # truly absent
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# Otherwise, print available values (partial allowed)
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def fmt(v):
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return f"{int(round(v))}" if isinstance(v, (int, float)) else "—"
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ppv = pp.get("tps_mean") if pp else None
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tgv = tg.get("tps_mean") if tg else None
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return f"{fmt(ppv)} pp / {tgv:.1f} tg" if isinstance(tgv, (int, float)) \
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else f"{fmt(ppv)} pp / — tg"
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def best_env_for(mkey, test):
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best_env, best_val = None, -1
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for env in ENV_ORDER:
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row = chosen[mkey].get(env, {}).get(test)
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if not row or row.get("error"):
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continue
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v = row.get("tps_mean")
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if isinstance(v, (int, float)) and v > best_val:
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best_env, best_val = env, v
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return best_env
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# Fuzzy match helper
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def find_model_key(fuzzy):
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needle = norm_model(fuzzy)
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for k in all_models:
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if needle in k:
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return k
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return None
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def format_cell(pp_run, tg_run):
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if not pp_run or not tg_run:
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return "—"
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if pp_run["error"] or tg_run["error"]:
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return ERROR_LABELS.get(pp_run["error_type"] or tg_run["error_type"], "⚠️ Error")
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if pp_run["tps_mean"] is None or tg_run["tps_mean"] is None:
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return "—"
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return f"{int(round(pp_run['tps_mean']))} pp / {tg_run['tps_mean']:.1f} tg"
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# Print table
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header = ["Model"] + [COL_NAMES[e] for e in ENV_ORDER] + ["🏆 Best PP", "🏆 Best TG"]
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print("| " + " | ".join(header) + " |")
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print("|" + "|".join(["---"] * len(header)) + "|")
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def find_winner(runs, model_prefix, bench_type):
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vals = {}
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for disp, fuzzy in DEFAULT_MODELS:
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mkey = find_model_key(fuzzy)
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if not mkey:
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print("| " + " | ".join([f"**{disp}**"] + ["—"]*len(ENV_ORDER) + ["—","—"]) + " |")
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continue
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row = [f"**{disp}**"]
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for env in ENV_ORDER:
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r = filter_runs(runs, model_prefix, env)
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if r and not r["error"] and r["test"] == bench_type and r["tps_mean"] is not None:
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vals[env] = r["tps_mean"]
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if not vals:
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return None
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return max(vals, key=vals.get)
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# --- Main ---
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def main():
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runs = load_results()
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header = ["Model"] + [COL_NAMES[e] for e in ENV_ORDER] + ["🏆 Best PP", "🏆 Best TG"]
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print("| " + " | ".join(header) + " |")
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print("|" + "|".join(["---"] * len(header)) + "|")
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for disp_name, model_prefix in DEFAULT_MODELS:
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row = [f"**{disp_name}**"]
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for env in ENV_ORDER:
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pp_run = filter_runs(runs, model_prefix, env)
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tg_run = filter_runs(runs, model_prefix, env)
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pp = None
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tg = None
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if pp_run and pp_run["test"] == "pp512":
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pp = pp_run
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if tg_run and tg_run["test"] == "tg128":
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tg = tg_run
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# match pp and tg runs by env
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pp_env_run = next((r for r in runs if r["model_clean"].startswith(model_prefix) and r["env"] == env and r["test"] == "pp512"), None)
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tg_env_run = next((r for r in runs if r["model_clean"].startswith(model_prefix) and r["env"] == env and r["test"] == "tg128"), None)
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row.append(format_cell(pp_env_run, tg_env_run))
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bpp = find_winner(runs, model_prefix, "pp512")
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btg = find_winner(runs, model_prefix, "tg128")
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row.append(f"🏆 **{WINNER_LABELS[bpp]}**" if bpp else "—")
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row.append(f"🏆 **{WINNER_LABELS[btg]}**" if btg else "—")
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print("| " + " | ".join(row) + " |")
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print("\nFull interactive results: [Live Benchmark Viewer](https://your-live-results-url)")
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if __name__ == "__main__":
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main()
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row.append(format_cell(chosen[mkey].get(env, {})))
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bpp = best_env_for(mkey, "pp512")
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btg = best_env_for(mkey, "tg128")
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row.append(f"🏆 **{WINNER_NAMES[bpp]}**" if bpp else "—")
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row.append(f"🏆 **{WINNER_NAMES[btg]}**" if btg else "—")
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print("| " + " | ".join(row) + " |")
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