cuda+engine: full fmt=4 (grouped int4 gs=64) support + diagnostic harness
The new g64 model quantizes experts with group-size 64 (fmt=4): one f32 scale per 64 elements instead of per-row. This required changes across the entire compute stack — CUDA kernel, engine dispatch, and dequant helpers — plus a new fused gate+up kernel to recover the ~40% throughput that the generic grouped path costs. CUDA backend (backend_cuda.cu): - ColiCudaTensor: add gs/ng fields for group-size metadata - row_bytes/weight_at: fmt=4 uses same packed int4 layout as fmt=2 - quant_matmul: apply per-group scales (scales[o*ng+g]) for fmt=4, matching the CPU matmul_i4_grouped accumulation exactly - coli_cuda_tensor_upload: allocate O*ng scales (not O) for fmt=4, store gs/ng on the tensor - coli_cuda_tensor_update: handle grouped scales on refresh - coli_cuda_tensor_free/tensor_bytes: VRAM accounting uses O*ng - coli_cuda_expert_group: return 0 for fmt=4 (fall back to correct per-expert path, since grouped_hidden/grouped_down kernels only handle per-row scales) - All 11 quant_matmul call sites updated to pass gs,ng Engine (glm.c): - qt_cuda_upload: pass t->gs to tensor_upload - matmul dispatch (line 816): pass w->gs to coli_cuda_matmul - kv_b shard upload: pass l->kv_b.gs - kv_b shard rb computation: add fmt=4 case ((I+1)/2) - embed_row: add fmt=4 branch with per-group scale dequant - qt_addrow/qt_matvec_rows: add fmt=4 branches (CPU MLA absorption path) - expert_gate_up: add matmul_i4_grouped_pair — fused gate+up for fmt=4 that reads x once instead of twice (+40% tok/s, 0.77 -> 1.08) - run_text: prepend [gMASK]<sop> prefix for GLM models (#108 fix — without it, PROMPT mode generates garbage on all GLM snapshots) API (backend_cuda.h, backend_loader.c): - coli_cuda_tensor_upload and coli_cuda_matmul signatures: add gs param - Loader typedefs and wrappers thread gs through the DLL boundary Tests: - bench_tensor_core.cu, test_backend_cuda.cu, test_pipe_cuda.cu: update all calls to new API signature (append gs=0 for non-grouped) New tool: c/tools/diag_harness.py - Comprehensive model diagnostic harness (system probe, correctness smoke, deep PROFILE diagnostic, quality benchmarks via eval_glm.py, throughput with/without MTP). Outputs JSON + Markdown reports. Performance (GLM-5.2 744B g64 / RTX 5070 Ti / 32GB RAM): broken CUDA: 0.05 tok/s (every tensor fell back to CPU) fixed CUDA: 0.30 tok/s (6x — CUDA working) + full opt stack: 0.77 tok/s (CACHE_ROUTE + EXPERT_BUDGET) + fused grouped pair: 1.08 tok/s (+40% from gate+up fusion)
This commit is contained in:
woolcoxm
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#!/usr/bin/env python3
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"""
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diag_harness.py — Comprehensive model diagnostic harness for the colibri GLM-5.2 engine.
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Runs a full campaign of tests against any model snapshot:
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Phase 0 (system): startup telemetry — GPU, RAM, cache cap, load time, MTP, idot kernel
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Phase 1 (smoke): correctness — curated prompts, coherence checks, corruption detection
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Phase 2 (diagnostic): deep x-ray — full PROFILE breakdown, routing, MTP, disk-split, CUDA tier
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Phase 3 (quality): benchmark accuracy — hellaswag/arc_challenge/mmlu via eval_glm.py SCORE
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Phase 4 (throughput): tok/s with and without MTP speculation
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Phase 5 (report): structured JSON + human-readable Markdown summary
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Usage:
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python tools/diag_harness.py --snap /path/to/model --phase all
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python tools/diag_harness.py --snap /path/to/model --phase smoke --ngen 64
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python tools/diag_harness.py --snap /path/to/model --phase quality --quality-limit 200
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Output goes to --out (default ./diag_results/<timestamp>/). Each phase writes a raw log
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(<phase>_<run>.log) and all metrics are collected into report.json + report.md.
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Design notes:
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- stdout and stderr are captured separately via subprocess.PIPE. The engine streams
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generated text to stdout (interleaved with prompt + PROFILE stats), but TOKENS=1 dumps
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clean token-id lists to stderr — that is the primary text-capture path.
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- Every regex is anchored to the exact printf format strings in glm.c (verified against
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profile_print line 3853, run_text line 3948, the banner line 5299, etc.).
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- Subprocess calls have a hard timeout (default 600s) and are killed cleanly on expiry.
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- A single phase can be run standalone; results accumulate in the output dir.
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"""
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import os, sys, re, json, time, argparse, subprocess, signal, traceback
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from datetime import datetime
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from pathlib import Path
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# ---------------------------------------------------------------------------
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# PROMPT SUITE — curated across categories. "expect" is a case-insensitive
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# substring checked against the generated text (None = coherence-only check).
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# ---------------------------------------------------------------------------
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PROMPTS = [
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{"id":"fact_capital", "cat":"factual", "prompt":"The capital of France is",
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"expect":"Paris", "note":"basic world knowledge"},
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{"id":"fact_boiling", "cat":"factual", "prompt":"What is the boiling point of water in Celsius?",
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"expect":"100", "note":"basic science"},
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{"id":"fact_planet", "cat":"factual", "prompt":"What planet is closest to the Sun?",
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"expect":"Mercury","note":"astronomy fact"},
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{"id":"math_mult", "cat":"math", "prompt":"What is 15 times 12?",
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"expect":"180", "note":"2-digit multiplication"},
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{"id":"math_add", "cat":"math", "prompt":"What is 847 plus 153?",
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"expect":"1000", "note":"3-digit addition"},
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{"id":"reason_train", "cat":"reasoning", "prompt":"If a train travels 60 mph for 2.5 hours, how far does it go?",
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"expect":"150", "note":"rate-time-distance"},
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{"id":"code_factorial","cat":"code", "prompt":"Write a Python function that computes the factorial of a number.",
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"expect":"def", "note":"code generation"},
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{"id":"explain_nn", "cat":"explanation","prompt":"Explain what a neural network is in one sentence.",
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"expect":None, "note":"coherence check"},
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{"id":"creative_story","cat":"creative", "prompt":"Write a one-sentence story about a lighthouse.",
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"expect":None, "note":"creative coherence"},
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{"id":"edge_hello", "cat":"edge", "prompt":"Hello, how are you today?",
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"expect":None, "note":"conversational opener"},
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{"id":"edge_the", "cat":"edge", "prompt":"The weather today is",
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"expect":None, "note":"simple continuation"},
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{"id":"edge_repeat", "cat":"edge", "prompt":"The quick brown fox jumps over the lazy dog. The quick brown fox",
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"expect":None, "note":"repetition-bait"},
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]
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# ---------------------------------------------------------------------------
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# METRIC EXTRACTION — each parser is (name, compiled_regex, group_index, cast).
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# Regexes match the EXACT printf format strings in glm.c.
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# ---------------------------------------------------------------------------
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def _f1(g): return float(g)
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def _i1(g): return int(g)
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# Build parsers as (regex, lambda(match)->value) for clarity
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RX = {
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# stdout: run_text summary line (line 3948)
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"decode_toks": (re.compile(r"decode (\d+) tokens in"), lambda m: int(m.group(1))),
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"decode_secs": (re.compile(r"decode \d+ tokens in ([\d.]+)s"), lambda m: float(m.group(1))),
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"decode_tps": (re.compile(r"decode \d+ tokens in [\d.]+s \(([\d.]+) tok/s\)"), lambda m: float(m.group(1))),
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"prefill_toks": (re.compile(r"prefill (\d+) tokens in"), lambda m: int(m.group(1))),
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"prefill_secs": (re.compile(r"prefill \d+ tokens in ([\d.]+)s"), lambda m: float(m.group(1))),
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"hit_rate": (re.compile(r"expert hit rate ([\d.]+)%"), lambda m: float(m.group(1))),
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"rss_gb": (re.compile(r"RSS ([\d.]+) GB"), lambda m: float(m.group(1))),
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"experts_per_tok":(re.compile(r"experts loaded/token: ([\d.]+)"), lambda m: float(m.group(1))),
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"mtp_accept": (re.compile(r"MTP acceptance ([\d.]+)%"), lambda m: float(m.group(1))),
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"mtp_acc_cnt": (re.compile(r"MTP acceptance \d+% \((\d+)/(\d+)\)"), lambda m: (int(m.group(1)), int(m.group(2)))),
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"spec_tok_per_fw":(re.compile(r"speculation: ([\d.]+) tokens/forward"),lambda m: float(m.group(1))),
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# stdout: PROFILE lines (profile_print, line 3853-3864)
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"prof_expert_disk": (re.compile(r"expert-disk ([\d.]+)s service"), lambda m: float(m.group(1))),
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"prof_expert_wait": (re.compile(r"service / ([\d.]+)s wait"), lambda m: float(m.group(1))),
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"prof_expert_mm": (re.compile(r"expert-matmul ([\d.]+)s"), lambda m: float(m.group(1))),
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"prof_attention": (re.compile(r"\| attention ([\d.]+)s"), lambda m: float(m.group(1))),
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"prof_kvb": (re.compile(r"including kvb ([\d.]+)s"), lambda m: float(m.group(1))),
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"prof_lm_head": (re.compile(r"lm_head ([\d.]+)s"), lambda m: float(m.group(1))),
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"prof_other": (re.compile(r"\| other ([\d.]+)s"), lambda m: float(m.group(1))),
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# stdout: banner (line 5299)
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"load_secs": (re.compile(r"loaded in ([\d.]+)s"), lambda m: float(m.group(1))),
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"resident_mb": (re.compile(r"resident dense: ([\d.]+) MB"), lambda m: float(m.group(1))),
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"mtp_status": (re.compile(r"MTP (ACTIVE|absent)"), lambda m: m.group(1)),
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"n_layers": (re.compile(r"layers=(\d+) experts=(\d+)"), lambda m: int(m.group(1))),
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"n_experts": (re.compile(r"layers=(\d+) experts=(\d+)"), lambda m: int(m.group(2))),
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# stdout: banner idot kernel
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"idot_kernel": (re.compile(r"idot: (\S+) =="), lambda m: m.group(1)),
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"cache_cap": (re.compile(r"cache=(\d+) experts/layer"), lambda m: int(m.group(1))),
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# stderr: RAM_GB (line 5086-5112)
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"ram_budget": (re.compile(r"\[RAM_GB=([\d.]+)"), lambda m: float(m.group(1))),
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"cap_lowered": (re.compile(r"cap lowered (\d+)->(\d+)"), lambda m: (int(m.group(1)), int(m.group(2)))),
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"cap_raised": (re.compile(r"cap raised (\d+)->(\d+)"), lambda m: (int(m.group(1)), int(m.group(2)))),
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"cap_ok": (re.compile(r"cap=(\d+) ok"), lambda m: int(m.group(1))),
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# stderr: CUDA (backend_cuda.cu:389)
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"cuda_device": (re.compile(r"\[CUDA\] device (\d+): (.*?), ([\d.]+) GB VRAM, sm_(\d)(\d)"),
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lambda m: {"id":int(m.group(1)),"name":m.group(2).strip(),"vram_gb":float(m.group(3)),
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"sm":f"{m.group(4)}.{m.group(5)}"}),
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"cuda_mode": (re.compile(r"\[CUDA\] mode: (.+)"), lambda m: m.group(1)),
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"cuda_tier": (re.compile(r"CUDA expert tier: (\d+) resident experts \(([\d.]+) GB\)"),
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lambda m: {"resident":int(m.group(1)),"vram_gb":float(m.group(2))}),
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# stderr: TOKENS dump (line 4010-4012)
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"tokens_dump": (re.compile(r"^\[TOKENS\] (\d+) generated:(.*)$", re.MULTILINE),
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lambda m: [int(x) for x in m.group(2).split()]),
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# stderr: per-16-token progress (emit_stream line 3742)
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"progress_tps": (re.compile(r"t=(\d+)\s+RSS ([\d.]+) GB\s+hit ([\d.]+)%\s+([\d.]+) tok/s\s+([\d.]+) tok/fw"),
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lambda m: {"tok":int(m.group(1)),"rss":float(m.group(2)),"hit":float(m.group(3)),
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"tps":float(m.group(4)),"tpf":float(m.group(5))}),
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# stderr: DSA, USAGE, KV startup lines
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"usage_loaded": (re.compile(r"\[USAGE\].*?(\d+) selections"), lambda m: int(m.group(1))),
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"kv_slots": (re.compile(r"\[KV\].*?(\d+) context slots"), lambda m: int(m.group(1))),
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}
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def extract_metrics(stdout: str, stderr: str) -> dict:
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"""Parse all metrics from the engine's stdout+stderr output."""
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text_out = stdout or ""
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text_err = stderr or ""
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metrics = {}
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# For most parsers we search BOTH streams (engine is inconsistent about which
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# channel a given line lands on). Some are stream-specific (noted below).
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combined = text_out + "\n" + text_err
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for name, (rx, fn) in RX.items():
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m = rx.search(combined)
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if m:
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try: metrics[name] = fn(m)
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except (ValueError, IndexError): pass
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# TOKENS dump is stderr-only and may appear once; grab it explicitly
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m = RX["tokens_dump"][0].search(text_err)
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if m:
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try: metrics["tokens_dump"] = RX["tokens_dump"][1](m)
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except: pass
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# Multiple CUDA devices — collect all
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cuda_devs = []
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for m in re.finditer(r"\[CUDA\] device (\d+): (.*?), ([\d.]+) GB VRAM, sm_(\d)(\d)", text_err):
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cuda_devs.append({"id":int(m.group(1)),"name":m.group(2).strip(),
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"vram_gb":float(m.group(3)),"sm":f"{m.group(4)}.{m.group(5)}"})
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if cuda_devs: metrics["cuda_devices"] = cuda_devs
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# Multiple progress checkpoints — collect the full curve
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progress = []
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for m in RX["progress_tps"][0].finditer(text_err):
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try:
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progress.append(RX["progress_tps"][1](m))
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except: pass
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if progress: metrics["progress_curve"] = progress
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# Multiple PROFILE lines — there are two (prefill + decode). Keep both.
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prof_lines = [(i, line) for i, line in enumerate(text_out.splitlines()) if line.startswith("PROFILE:")]
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for idx, (line_no, line) in enumerate(prof_lines):
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label = "prefill_profile" if idx == 0 else "decode_profile"
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p = {}
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for pname, (rx, fn) in RX.items():
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if not pname.startswith("prof_"): continue
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mm = rx.search(line)
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if mm:
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try: p[pname] = fn(mm)
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except: pass
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if p: metrics[label] = p
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return metrics
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# ---------------------------------------------------------------------------
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# ENGINE RUNNER — subprocess wrapper with timeout, signal handling, logging.
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# ---------------------------------------------------------------------------
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class EngineRunner:
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def __init__(self, glm_path, snap, out_dir, default_env=None, timeout=600):
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self.glm = str(glm_path)
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self.snap = str(snap)
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self.out_dir = Path(out_dir)
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self.out_dir.mkdir(parents=True, exist_ok=True)
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self.default_env = default_env or {}
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self.timeout = timeout
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self.default_cap = 75 # production default (matches bench_full.sh)
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def run_prompt(self, prompt, ngen=64, env_extra=None, log_name=None, cap=None):
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"""Run the engine in PROMPT mode and return (stdout, stderr, returncode, elapsed)."""
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env = dict(os.environ, SNAP=self.snap, PROMPT=prompt, NGEN=str(ngen))
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env.update(self.default_env)
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if env_extra: env.update(env_extra)
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env["TOKENS"] = "1" # always capture token ids for reliable text extraction
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cmd = [self.glm, str(cap if cap is not None else self.default_cap)]
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return self._exec(cmd, env, log_name)
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def run_score(self, score_file, cap=None, env_extra=None, log_name=None):
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"""Run the engine in SCORE (log-likelihood) mode."""
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env = dict(os.environ, SNAP=self.snap, SCORE=score_file)
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env.update(self.default_env)
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if env_extra: env.update(env_extra)
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cmd = [self.glm, str(cap if cap is not None else self.default_cap)]
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return self._exec(cmd, env, log_name)
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def _exec(self, cmd, env, log_name):
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t0 = time.time()
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log_path = self.out_dir / (log_name or f"run_{int(t0)}.log")
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try:
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proc = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
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text=True, encoding="utf-8", errors="replace")
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try:
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stdout, stderr = proc.communicate(timeout=self.timeout)
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rc = proc.returncode
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except subprocess.TimeoutExpired:
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proc.kill()
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stdout, stderr = proc.communicate()
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rc = -1
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stderr = (stderr or "") + f"\n[TIMEOUT after {self.timeout}s]\n"
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except Exception as e:
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stdout, stderr, rc = "", f"[EXCEPTION] {e}\n{traceback.format_exc()}", -2
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elapsed = time.time() - t0
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# Write raw log (both streams, clearly delimited)
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with open(log_path, "w", encoding="utf-8") as f:
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f.write(f"=== CMD: {' '.join(cmd)}\n=== ELAPSED: {elapsed:.1f}s\n=== RC: {rc}\n\n")
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f.write("--- STDOUT ---\n"); f.write(stdout or ""); f.write("\n")
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f.write("--- STDERR ---\n"); f.write(stderr or ""); f.write("\n")
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return stdout or "", stderr or "", rc, elapsed
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# ---------------------------------------------------------------------------
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# TEXT RECOVERY — decode the [TOKENS] ids back to text using the tokenizer.
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# Falls back to stdout extraction if tokenizer/tokens unavailable.
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# ---------------------------------------------------------------------------
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def recover_text(stdout, stderr, prompt, tokenizer=None):
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"""Extract generated text. Primary: TOKENS dump decoded. Fallback: stdout parse."""
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# Method 1: decode TOKENS ids via tokenizer
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if tokenizer:
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m = re.search(r"^\[TOKENS\] \d+ generated:(.*)$", stderr, re.MULTILINE)
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if m:
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ids = [int(x) for x in m.group(1).split()]
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if ids:
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try:
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return tokenizer.decode(ids), "tokens"
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except Exception: pass
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# Method 2: stdout — text is between the prompt string and "PROFILO" or "\n---"
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text = stdout or ""
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# Find the prompt in stdout, take everything after it up to PROFILO
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idx = text.find(prompt)
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if idx >= 0:
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after = text[idx + len(prompt):]
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cut = after.find("PROFILO")
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if cut >= 0:
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return after[:cut].strip(), "stdout"
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cut = after.find("\n---")
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if cut >= 0:
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return after[:cut].strip(), "stdout"
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return "", "none"
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# ---------------------------------------------------------------------------
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# CORRUPTION / COHERENCE CHECKS
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# ---------------------------------------------------------------------------
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def check_repetition(token_ids):
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"""Detect degenerate repetition: same token 3+ consecutive times."""
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if not token_ids or len(token_ids) < 6:
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return False, 0
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max_run = 1; cur = 1
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for i in range(1, len(token_ids)):
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if token_ids[i] == token_ids[i-1]: cur += 1; max_run = max(max_run, cur)
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else: cur = 1
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return max_run >= 3, max_run
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def check_expected(text, expect):
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"""Check if expected substring appears case-insensitively in the first 200 chars."""
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if not expect or not text: return None
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return expect.lower() in text[:200].lower()
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# ---------------------------------------------------------------------------
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# PHASES
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# ---------------------------------------------------------------------------
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class DiagnosticHarness:
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def __init__(self, args):
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self.args = args
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self.snap = args.snap
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self.glm = args.glm
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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self.out_dir = Path(args.out or f"./diag_results/{ts}")
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self.out_dir.mkdir(parents=True, exist_ok=True)
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# Base env for all runs
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base_env = {"TEMP": "0", "PIPE": "1", "PIPE_WORKERS": "8", "DIRECT": "1"}
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if args.ram: base_env["RAM_GB"] = str(args.ram)
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if args.cuda:
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base_env.update({
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"COLI_CUDA": "1",
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"COLI_GPU": str(args.gpu) if args.gpu is not None else "0",
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"CUDA_DENSE": "1",
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"COLI_CUDA_ATTN": "1",
|
||||
"COLI_CUDA_PIPE": "2",
|
||||
"COLI_CUDA_PIPE_S_MIN": "1",
|
||||
})
|
||||
self.runner = EngineRunner(self.glm, self.snap, self.out_dir, base_env, timeout=args.timeout)
|
||||
self.runner.default_cap = args.cap
|
||||
# Load tokenizer for text recovery
|
||||
self.tokenizer = None
|
||||
tok_path = os.path.join(self.snap, "tokenizer.json")
|
||||
if os.path.exists(tok_path):
|
||||
try:
|
||||
from tokenizers import Tokenizer
|
||||
self.tokenizer = Tokenizer.from_file(tok_path)
|
||||
except Exception as e:
|
||||
print(f"[warn] could not load tokenizer: {e}", file=sys.stderr)
|
||||
self.results = {"meta": {"snap": self.snap, "glm": self.glm,
|
||||
"timestamp": ts, "args": vars(args)},
|
||||
"phases": {}}
|
||||
|
||||
def phase_system(self):
|
||||
"""Phase 0: minimal run to capture startup telemetry."""
|
||||
print("\n" + "="*60 + "\nPHASE 0: SYSTEM PROBE\n" + "="*60)
|
||||
stdout, stderr, rc, elapsed = self.runner.run_prompt(
|
||||
"Hello", ngen=1, log_name="system_probe.log")
|
||||
metrics = extract_metrics(stdout, stderr)
|
||||
result = {
|
||||
"rc": rc, "elapsed": elapsed,
|
||||
"load_secs": metrics.get("load_secs"),
|
||||
"resident_mb": metrics.get("resident_mb"),
|
||||
"n_layers": metrics.get("n_layers"),
|
||||
"n_experts": metrics.get("n_experts"),
|
||||
"mtp_status": metrics.get("mtp_status"),
|
||||
"idot_kernel": metrics.get("idot_kernel"),
|
||||
"cache_cap_requested": metrics.get("cache_cap"),
|
||||
"ram_budget_gb": metrics.get("ram_budget"),
|
||||
"cap_lowered": metrics.get("cap_lowered"),
|
||||
"cap_raised": metrics.get("cap_raised"),
|
||||
"cap_final": metrics.get("cap_ok"),
|
||||
"cuda_devices": metrics.get("cuda_devices", []),
|
||||
"cuda_mode": metrics.get("cuda_mode"),
|
||||
}
|
||||
# Print summary
|
||||
print(f" load time: {result['load_secs']:.2f}s" if result['load_secs'] else " load time: FAILED")
|
||||
print(f" resident: {result['resident_mb']:.0f} MB" if result['resident_mb'] else "")
|
||||
print(f" layers/exp: {result['n_layers']}/{result['n_experts']}" if result['n_layers'] else "")
|
||||
print(f" MTP: {result['mtp_status']}")
|
||||
print(f" idot kernel: {result['idot_kernel']}")
|
||||
print(f" RAM budget: {result['ram_budget_gb']} GB" if result['ram_budget_gb'] else " RAM budget: auto")
|
||||
if result['cap_lowered']:
|
||||
print(f" cache cap: {result['cap_lowered'][0]} -> {result['cap_lowered'][1]} (RAM-lowered)")
|
||||
elif result['cap_final']:
|
||||
print(f" cache cap: {result['cap_final']} (ok)")
|
||||
else:
|
||||
print(f" cache cap: {result['cache_cap_requested']}")
|
||||
for d in result['cuda_devices']:
|
||||
print(f" GPU {d['id']}: {d['name']}, {d['vram_gb']:.1f} GB, sm_{d['sm']}")
|
||||
if rc != 0 and rc != -1:
|
||||
print(f" WARNING: engine returned rc={rc}")
|
||||
self.results["phases"]["system"] = result
|
||||
return result
|
||||
|
||||
def phase_smoke(self):
|
||||
"""Phase 1: correctness smoke test across curated prompts."""
|
||||
print("\n" + "="*60 + "\nPHASE 1: CORRECTNESS SMOKE TEST\n" + "="*60)
|
||||
ngen = self.args.ngen
|
||||
prompt_results = []
|
||||
pass_count = 0; total = len(PROMPTS)
|
||||
for p in PROMPTS:
|
||||
stdout, stderr, rc, elapsed = self.runner.run_prompt(
|
||||
p["prompt"], ngen=ngen, log_name=f"smoke_{p['id']}.log")
|
||||
metrics = extract_metrics(stdout, stderr)
|
||||
token_ids = metrics.get("tokens_dump", [])
|
||||
text, method = recover_text(stdout, stderr, p["prompt"], self.tokenizer)
|
||||
is_rep, max_run = check_repetition(token_ids)
|
||||
expect_ok = check_expected(text, p.get("expect"))
|
||||
# Verdict: pass if text is non-empty, no severe repetition, and (if factual) expected found
|
||||
has_text = bool(text.strip())
|
||||
ok = has_text and not is_rep
|
||||
if p.get("expect") and expect_ok is False: ok = False
|
||||
if ok: pass_count += 1
|
||||
# Diagnose failure mode for reporting
|
||||
if not has_text:
|
||||
fail_reason = "no tokens generated (immediate EOS)"
|
||||
elif is_rep:
|
||||
fail_reason = f"repetition loop (max run={max_run})"
|
||||
elif p.get("expect") and expect_ok is False:
|
||||
fail_reason = f"expected '{p['expect']}' not found"
|
||||
else:
|
||||
fail_reason = ""
|
||||
prompt_results.append({
|
||||
"id": p["id"], "cat": p["cat"], "prompt": p["prompt"],
|
||||
"expect": p.get("expect"), "generated": text[:300],
|
||||
"expect_match": expect_ok, "repetition": is_rep, "max_run": max_run,
|
||||
"toks_generated": len(token_ids), "decode_tps": metrics.get("decode_tps"),
|
||||
"hit_rate": metrics.get("hit_rate"), "rc": rc, "elapsed": elapsed,
|
||||
"text_method": method, "pass": ok, "fail_reason": fail_reason,
|
||||
})
|
||||
status = "PASS" if ok else "FAIL"
|
||||
extra = f" expect={'Y' if expect_ok else ('N' if expect_ok is False else '-')}" if p.get("expect") else ""
|
||||
tps = f" {metrics.get('decode_tps',0):.2f}t/s" if metrics.get("decode_tps") else ""
|
||||
print(f" [{status}] {p['id']:<22} ({p['cat']:<11}) rep={'Y' if is_rep else 'N'}{extra}{tps}")
|
||||
if text:
|
||||
preview = text.replace("\n", " ")[:80]
|
||||
print(f" -> {preview}")
|
||||
elif rc != 0:
|
||||
print(f" -> [engine rc={rc}]")
|
||||
else:
|
||||
print(f" -> [{fail_reason}]")
|
||||
result = {"prompts": prompt_results, "pass": pass_count, "total": total,
|
||||
"pass_rate": 100.0 * pass_count / total if total else 0}
|
||||
print(f"\n SMOKE SUMMARY: {pass_count}/{total} passed ({result['pass_rate']:.0f}%)")
|
||||
self.results["phases"]["smoke"] = result
|
||||
return result
|
||||
|
||||
def phase_diagnostic(self):
|
||||
"""Phase 2: single deep-instrumented run — full PROFILE + routing + MTP."""
|
||||
print("\n" + "="*60 + "\nPHASE 2: FULL SYSTEM DIAGNOSTIC\n" + "="*60)
|
||||
diag_env = {
|
||||
"LOOKA": "1", "DISK_SPLIT": "1", "ROUTE_AGREE": "1",
|
||||
}
|
||||
if self.args.cuda:
|
||||
diag_env["COLI_CUDA_PROFILE"] = "1"
|
||||
prompt = ("Write a short paragraph explaining how photosynthesis works. "
|
||||
"Include the roles of sunlight, water, and carbon dioxide.")
|
||||
stdout, stderr, rc, elapsed = self.runner.run_prompt(
|
||||
prompt, ngen=self.args.ngen, env_extra=diag_env, log_name="diagnostic.log")
|
||||
metrics = extract_metrics(stdout, stderr)
|
||||
text, _ = recover_text(stdout, stderr, prompt, self.tokenizer)
|
||||
result = {
|
||||
"rc": rc, "elapsed": elapsed,
|
||||
"generated_text": text[:500],
|
||||
"prefill_profile": metrics.get("prefill_profile", {}),
|
||||
"decode_profile": metrics.get("decode_profile", {}),
|
||||
"decode_tps": metrics.get("decode_tps"),
|
||||
"prefill_secs": metrics.get("prefill_secs"),
|
||||
"hit_rate": metrics.get("hit_rate"),
|
||||
"rss_gb": metrics.get("rss_gb"),
|
||||
"experts_per_tok": metrics.get("experts_per_tok"),
|
||||
"mtp_accept": metrics.get("mtp_accept"),
|
||||
"mtp_counts": metrics.get("mtp_acc_cnt"),
|
||||
"spec_tok_per_fw": metrics.get("spec_tok_per_fw"),
|
||||
"cuda_tier": metrics.get("cuda_tier"),
|
||||
"progress_curve": metrics.get("progress_curve", []),
|
||||
}
|
||||
# Print the PROFILE breakdown
|
||||
dp = result["decode_profile"]
|
||||
print(f"\n DECODE TIMING BREAKDOWN (per-bucket, seconds):")
|
||||
print(f" expert-disk: {dp.get('prof_expert_disk', '?'):>8}")
|
||||
print(f" expert-matmul: {dp.get('prof_expert_mm', '?'):>8}")
|
||||
print(f" attention: {dp.get('prof_attention', '?'):>8}")
|
||||
print(f" lm_head: {dp.get('prof_lm_head', '?'):>8}")
|
||||
print(f" other: {dp.get('prof_other', '?'):>8}")
|
||||
print(f"\n PERFORMANCE:")
|
||||
print(f" prefill: {result['prefill_secs']:.2f}s" if result['prefill_secs'] else " prefill: ?")
|
||||
print(f" decode: {result['decode_tps']:.3f} tok/s" if result['decode_tps'] else " decode: ?")
|
||||
print(f" hit rate: {result['hit_rate']:.1f}%" if result.get('hit_rate') is not None else " hit rate: ?")
|
||||
print(f" RSS: {result['rss_gb']:.1f} GB" if result.get('rss_gb') else " RSS: ?")
|
||||
print(f" exp/tok: {result['experts_per_tok']:.1f}" if result.get('experts_per_tok') else " exp/tok: ?")
|
||||
print(f" MTP: {result['mtp_accept']:.0f}% accept" if result.get('mtp_accept') is not None else " MTP: ?")
|
||||
if result.get('cuda_tier'):
|
||||
ct = result['cuda_tier']
|
||||
print(f" CUDA tier: {ct['resident']} experts, {ct['vram_gb']:.1f} GB VRAM")
|
||||
# Show generated text preview
|
||||
if text:
|
||||
print(f"\n GENERATED TEXT (first 200 chars):")
|
||||
print(f" {text[:200].replace(chr(10), ' ')}")
|
||||
else:
|
||||
print(f"\n GENERATED TEXT: [none recovered]")
|
||||
self.results["phases"]["diagnostic"] = result
|
||||
return result
|
||||
|
||||
def phase_quality(self):
|
||||
"""Phase 3: benchmark accuracy via eval_glm.py SCORE mode."""
|
||||
print("\n" + "="*60 + "\nPHASE 3: QUALITY BENCHMARKS\n" + "="*60)
|
||||
eval_script = os.path.join(os.path.dirname(__file__), "eval_glm.py")
|
||||
bench_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "bench")
|
||||
if not os.path.exists(eval_script):
|
||||
print(f" [SKIP] eval_glm.py not found at {eval_script}")
|
||||
self.results["phases"]["quality"] = {"error": "eval_glm.py not found"}
|
||||
return None
|
||||
tasks = ["hellaswag", "arc_challenge", "mmlu"]
|
||||
missing = [t for t in tasks if not os.path.exists(os.path.join(bench_dir, f"{t}.jsonl"))]
|
||||
if missing:
|
||||
print(f" [SKIP] benchmark data missing: {missing}")
|
||||
print(f" run: python tools/fetch_benchmarks.py --out {bench_dir}")
|
||||
self.results["phases"]["quality"] = {"error": f"missing benchmark data: {missing}"}
|
||||
return None
|
||||
limit = self.args.quality_limit
|
||||
py = sys.executable
|
||||
cmd = [py, eval_script, "--snap", self.snap, "--glm", self.glm,
|
||||
"--data", bench_dir, "--tasks", ",".join(tasks), "--limit", str(limit)]
|
||||
env = dict(os.environ)
|
||||
if self.args.ram: env["RAM_GB"] = str(self.args.ram)
|
||||
print(f" Running eval_glm.py (tasks={tasks}, n={limit})...")
|
||||
print(f" This takes ~{limit*3*4/0.05:.0f}s at 0.05 tok/s (worst case)...")
|
||||
t0 = time.time()
|
||||
log_path = self.out_dir / "quality_eval.log"
|
||||
try:
|
||||
with open(log_path, "w", encoding="utf-8") as logf:
|
||||
proc = subprocess.run(cmd, env=env, capture_output=True, text=True, timeout=self.args.timeout*3,
|
||||
encoding="utf-8", errors="replace")
|
||||
logf.write(proc.stdout); logf.write("\n--- STDERR ---\n"); logf.write(proc.stderr)
|
||||
elapsed = time.time() - t0
|
||||
# Parse the acc/acc_norm table from eval_glm.py output
|
||||
scores = {}
|
||||
for line in proc.stdout.splitlines():
|
||||
# lines like: "hellaswag 40 45.0% 50.0%"
|
||||
m = re.match(r"(\w+)\s+(\d+)\s+([\d.]+)%\s+([\d.]+)%", line.strip())
|
||||
if m:
|
||||
scores[m.group(1)] = {"n": int(m.group(2)), "acc": float(m.group(3)),
|
||||
"acc_norm": float(m.group(4))}
|
||||
mean_m = re.search(r"MEAN acc_norm:\s*([\d.]+)%", proc.stdout)
|
||||
result = {"scores": scores, "mean_acc_norm": float(mean_m.group(1)) if mean_m else None,
|
||||
"elapsed": elapsed, "rc": proc.returncode}
|
||||
print(f" Completed in {elapsed:.0f}s\n")
|
||||
print(f" {'task':<18} {'n':>4} {'acc':>7} {'acc_norm':>9}")
|
||||
for t, s in scores.items():
|
||||
print(f" {t:<18} {s['n']:>4} {s['acc']:>6.1f}% {s['acc_norm']:>8.1f}%")
|
||||
if result["mean_acc_norm"] is not None:
|
||||
print(f"\n MEAN acc_norm: {result['mean_acc_norm']:.1f}%")
|
||||
except subprocess.TimeoutExpired:
|
||||
result = {"error": f"eval timed out after {self.args.timeout*3}s"}
|
||||
print(f" [TIMEOUT] eval_glm.py exceeded {self.args.timeout*3}s")
|
||||
except Exception as e:
|
||||
result = {"error": str(e)}
|
||||
print(f" [ERROR] {e}")
|
||||
self.results["phases"]["quality"] = result
|
||||
return result
|
||||
|
||||
def phase_throughput(self):
|
||||
"""Phase 4: tok/s comparison — MTP on vs off."""
|
||||
print("\n" + "="*60 + "\nPHASE 4: THROUGHPUT BENCHMARK\n" + "="*60)
|
||||
prompt = "Summarize the plot of Romeo and Juliet in three sentences."
|
||||
ngen = self.args.ngen
|
||||
results = {}
|
||||
# Run 1: with MTP (default)
|
||||
print(f" [1/2] MTP ON (draft=3)...")
|
||||
stdout, stderr, rc, t = self.runner.run_prompt(
|
||||
prompt, ngen=ngen, log_name="throughput_mtp_on.log")
|
||||
m_on = extract_metrics(stdout, stderr)
|
||||
results["mtp_on"] = {"tps": m_on.get("decode_tps"), "hit": m_on.get("hit_rate"),
|
||||
"mtp_accept": m_on.get("mtp_accept"), "elapsed": t}
|
||||
print(f" {m_on.get('decode_tps',0):.3f} tok/s | hit {m_on.get('hit_rate',0):.1f}% | "
|
||||
f"MTP {m_on.get('mtp_accept',0):.0f}%")
|
||||
# Run 2: MTP off
|
||||
print(f" [2/2] MTP OFF (MTP=0)...")
|
||||
stdout, stderr, rc, t = self.runner.run_prompt(
|
||||
prompt, ngen=ngen, env_extra={"MTP": "0"}, log_name="throughput_mtp_off.log")
|
||||
m_off = extract_metrics(stdout, stderr)
|
||||
results["mtp_off"] = {"tps": m_off.get("decode_tps"), "hit": m_off.get("hit_rate"),
|
||||
"elapsed": t}
|
||||
print(f" {m_off.get('decode_tps',0):.3f} tok/s | hit {m_off.get('hit_rate',0):.1f}%")
|
||||
# Compute speedup
|
||||
if results["mtp_on"]["tps"] and results["mtp_off"]["tps"] and results["mtp_off"]["tps"] > 0:
|
||||
sp = results["mtp_on"]["tps"] / results["mtp_off"]["tps"]
|
||||
results["mtp_speedup"] = sp
|
||||
print(f"\n MTP speedup: {sp:.2f}x ({'MTP helps' if sp > 1.05 else 'MTP hurts' if sp < 0.95 else 'no effect'})")
|
||||
else:
|
||||
print(f"\n MTP speedup: (insufficient data)")
|
||||
self.results["phases"]["throughput"] = results
|
||||
return results
|
||||
|
||||
def write_report(self):
|
||||
"""Write report.json and report.md."""
|
||||
ts = self.results["meta"]["timestamp"]
|
||||
json_path = self.out_dir / "report.json"
|
||||
md_path = self.out_dir / "report.md"
|
||||
# JSON
|
||||
with open(json_path, "w", encoding="utf-8") as f:
|
||||
json.dump(self.results, f, indent=2, default=str)
|
||||
# Markdown
|
||||
lines = [
|
||||
f"# Diagnostic Report — {ts}",
|
||||
f"",
|
||||
f"**Model:** `{self.snap}`",
|
||||
f"**Engine:** `{self.glm}`",
|
||||
f"**Date:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
|
||||
f"",
|
||||
]
|
||||
phases = self.results["phases"]
|
||||
# System
|
||||
if "system" in phases:
|
||||
s = phases["system"]
|
||||
lines += ["## Phase 0: System Probe", ""]
|
||||
lines += [f"- Load time: **{s.get('load_secs','?')}s**"]
|
||||
lines += [f"- Layers/experts: {s.get('n_layers','?')}/{s.get('n_experts','?')}"]
|
||||
lines += [f"- MTP: {s.get('mtp_status','?')}"]
|
||||
lines += [f"- idot kernel: `{s.get('idot_kernel','?')}`"]
|
||||
lines += [f"- RAM budget: {s.get('ram_budget_gb','auto')} GB"]
|
||||
if s.get("cap_lowered"):
|
||||
lines += [f"- Cache cap: {s['cap_lowered'][0]}→{s['cap_lowered'][1]} (RAM-lowered)"]
|
||||
elif s.get("cap_final"):
|
||||
lines += [f"- Cache cap: {s['cap_final']}"]
|
||||
for d in s.get("cuda_devices", []):
|
||||
lines += [f"- GPU {d['id']}: {d['name']}, {d['vram_gb']:.1f} GB, sm_{d['sm']}"]
|
||||
lines.append("")
|
||||
# Smoke
|
||||
if "smoke" in phases:
|
||||
sm = phases["smoke"]
|
||||
lines += [f"## Phase 1: Correctness Smoke", "",
|
||||
f"**{sm['pass']}/{sm['total']} prompts passed ({sm['pass_rate']:.0f}%)**", "",
|
||||
"| ID | Category | Pass | Expect | Repetition | tok/s | Generated (first 80 chars) |",
|
||||
"|---|---|---|---|---|---|---|"]
|
||||
for p in sm["prompts"]:
|
||||
gen = p["generated"][:80].replace("|", "\\|").replace("\n", " ") if p["generated"] else ""
|
||||
exp = "Y" if p.get("expect_match") else ("N" if p.get("expect_match") is False else "-")
|
||||
tps = f"{p.get('decode_tps',0):.2f}" if p.get("decode_tps") else "-"
|
||||
lines.append(f"| {p['id']} | {p['cat']} | {'✅' if p['pass'] else '❌'} | {exp} | "
|
||||
f"{'⚠️' if p['repetition'] else '-'} (run={p.get('max_run',0)}) | {tps} | {gen} |")
|
||||
lines.append("")
|
||||
# Diagnostic
|
||||
if "diagnostic" in phases:
|
||||
d = phases["diagnostic"]
|
||||
lines += ["## Phase 2: Full System Diagnostic", ""]
|
||||
dp = d.get("decode_profile", {})
|
||||
lines += ["### Decode Timing Breakdown", "",
|
||||
"| Bucket | Seconds |", "|---|---|"]
|
||||
for k, label in [("prof_expert_disk","expert-disk"),("prof_expert_mm","expert-matmul"),
|
||||
("prof_attention","attention"),("prof_lm_head","lm_head"),
|
||||
("prof_other","other")]:
|
||||
v = dp.get(k, "?")
|
||||
lines.append(f"| {label} | {v} |")
|
||||
lines += [f"", f"### Performance", f"- Decode: **{d.get('decode_tps','?')} tok/s**",
|
||||
f"- Prefill: {d.get('prefill_secs','?')}s",
|
||||
f"- Expert hit rate: {d.get('hit_rate','?')}%",
|
||||
f"- RSS: {d.get('rss_gb','?')} GB",
|
||||
f"- Experts/token: {d.get('experts_per_tok','?')}",
|
||||
f"- MTP acceptance: {d.get('mtp_accept','?')}%", ""]
|
||||
if d.get("generated_text"):
|
||||
lines += [f"### Generated Text", f"```\n{d['generated_text'][:300]}\n```", ""]
|
||||
# Quality
|
||||
if "quality" in phases:
|
||||
q = phases["quality"]
|
||||
lines += ["## Phase 3: Quality Benchmarks", ""]
|
||||
if "error" in q:
|
||||
lines += [f"⚠️ {q['error']}", ""]
|
||||
else:
|
||||
lines += [f"**Mean acc_norm: {q.get('mean_acc_norm','?')}%**", "",
|
||||
"| Task | n | acc | acc_norm |", "|---|---|---|---|"]
|
||||
for t, s in q.get("scores", {}).items():
|
||||
lines.append(f"| {t} | {s['n']} | {s['acc']:.1f}% | {s['acc_norm']:.1f}% |")
|
||||
lines.append("")
|
||||
# Throughput
|
||||
if "throughput" in phases:
|
||||
th = phases["throughput"]
|
||||
lines += ["## Phase 4: Throughput", "",
|
||||
"| Mode | tok/s | hit% | MTP accept |", "|---|---|---|---|"]
|
||||
on = th.get("mtp_on", {}); off = th.get("mtp_off", {})
|
||||
lines.append(f"| MTP ON | {on.get('tps','?')} | {on.get('hit','?')}% | {on.get('mtp_accept','?')}% |")
|
||||
lines.append(f"| MTP OFF | {off.get('tps','?')} | {off.get('hit','?')}% | — |")
|
||||
if th.get("mtp_speedup"):
|
||||
lines.append(f"\n**MTP speedup: {th['mtp_speedup']:.2f}x**")
|
||||
lines.append("")
|
||||
with open(md_path, "w", encoding="utf-8") as f:
|
||||
f.write("\n".join(lines))
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Report written:")
|
||||
print(f" JSON: {json_path}")
|
||||
print(f" MD: {md_path}")
|
||||
print(f" Logs: {self.out_dir}/*.log")
|
||||
print(f"{'='*60}")
|
||||
|
||||
def run(self):
|
||||
phase = self.args.phase
|
||||
if phase == "all":
|
||||
self.phase_system()
|
||||
self.phase_smoke()
|
||||
self.phase_diagnostic()
|
||||
self.phase_quality()
|
||||
self.phase_throughput()
|
||||
elif phase == "system": self.phase_system()
|
||||
elif phase == "smoke": self.phase_smoke()
|
||||
elif phase == "diagnostic": self.phase_diagnostic()
|
||||
elif phase == "quality": self.phase_quality()
|
||||
elif phase == "throughput": self.phase_throughput()
|
||||
else:
|
||||
print(f"Unknown phase: {phase}", file=sys.stderr); sys.exit(1)
|
||||
self.write_report()
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description="Comprehensive model diagnostic harness for colibri GLM-5.2")
|
||||
ap.add_argument("--snap", required=True, help="model snapshot directory")
|
||||
ap.add_argument("--glm", default=None, help="engine binary path (default: ./glm.exe or ./glm)")
|
||||
ap.add_argument("--phase", default="all",
|
||||
choices=["all","system","smoke","diagnostic","quality","throughput"],
|
||||
help="which test phase to run")
|
||||
ap.add_argument("--out", default=None, help="output directory (default: ./diag_results/<timestamp>)")
|
||||
ap.add_argument("--ngen", type=int, default=64, help="generation length for smoke/throughput (default 64)")
|
||||
ap.add_argument("--quality-limit", type=int, default=40, help="questions per benchmark task (default 40)")
|
||||
ap.add_argument("--ram", type=float, default=0, help="RAM_GB override (0=auto)")
|
||||
ap.add_argument("--cuda", action="store_true", help="enable COLI_CUDA GPU tier")
|
||||
ap.add_argument("--gpu", type=int, default=None, help="GPU device ordinal (with --cuda)")
|
||||
ap.add_argument("--cap", type=int, default=75, help="experts-per-layer cache cap (default 75)")
|
||||
ap.add_argument("--timeout", type=int, default=600, help="per-run timeout in seconds (default 600)")
|
||||
a = ap.parse_args()
|
||||
# Auto-detect engine binary
|
||||
if a.glm is None:
|
||||
for cand in ["./glm.exe", "./glm", "../glm.exe", os.path.join(os.path.dirname(__file__), "..", "glm.exe")]:
|
||||
if os.path.exists(cand): a.glm = os.path.abspath(cand); break
|
||||
if a.glm is None:
|
||||
print("ERROR: could not find glm/glm.exe. Specify with --glm", file=sys.stderr); sys.exit(1)
|
||||
if not os.path.isdir(a.snap):
|
||||
print(f"ERROR: snapshot dir does not exist: {a.snap}", file=sys.stderr); sys.exit(1)
|
||||
harness = DiagnosticHarness(a)
|
||||
harness.run()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user