Initial release
This commit is contained in:
civ
@@ -0,0 +1,501 @@
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#!/usr/bin/env python3
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"""GGML vs Python cosine similarity comparison for ACE-Step DiT.
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Run from tests/ directory. All paths relative to CWD.
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Usage:
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cd tests/
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./debug-dit-cossim.py # turbo BF16
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./debug-dit-cossim.py --quant Q6_K # turbo Q6_K
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./debug-dit-cossim.py --mode sft # SFT BF16
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./debug-dit-cossim.py --mode xl-turbo # XL turbo BF16
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./debug-dit-cossim.py --mode all # all 4 models
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"""
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import os, sys, subprocess, struct, shutil, argparse, json
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import numpy as np
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SEED = 42
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MODE_CONFIG = {
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"turbo": {
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"gguf_base": "acestep-v15-turbo",
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"config_path": "acestep-v15-turbo",
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"steps": 8, "shift": 3.0, "guidance": 0.0, "n_layers": 24,
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},
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"sft": {
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"gguf_base": "acestep-v15-sft",
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"config_path": "acestep-v15-sft",
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"steps": 50, "shift": 1.0, "guidance": 1.0, "n_layers": 24,
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},
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"xl-turbo": {
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"gguf_base": "acestep-v15-xl-turbo",
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"config_path": "acestep-v15-xl-turbo",
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"steps": 8, "shift": 3.0, "guidance": 0.0, "n_layers": 32,
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},
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"xl-sft": {
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"gguf_base": "acestep-v15-xl-sft",
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"config_path": "acestep-v15-xl-sft",
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"steps": 50, "shift": 1.0, "guidance": 1.0, "n_layers": 32,
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},
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}
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def load_request():
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if not os.path.isfile("request0.json"):
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print("[Error] request0.json not found in CWD")
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sys.exit(1)
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with open("request0.json") as f:
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req = json.load(f)
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print(f"[Request] Loaded request0.json")
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return req
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def save_dump(path, data):
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import torch
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if isinstance(data, torch.Tensor):
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data = data.detach().float().cpu().numpy()
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data = np.ascontiguousarray(data.astype(np.float32))
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shape = data.shape
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header = struct.pack("i", len(shape))
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for s in shape:
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header += struct.pack("i", s)
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with open(path, "wb") as f:
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f.write(header)
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f.write(data.tobytes())
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def load_dump(path):
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raw = np.fromfile(path, dtype=np.float32)
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ndim = int(struct.unpack("i", struct.pack("f", raw[0]))[0])
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shape = [int(struct.unpack("i", struct.pack("f", raw[1+i]))[0]) for i in range(ndim)]
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data = raw[1 + ndim:]
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return data, shape
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def _cos_flat(a, b):
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n = min(len(a), len(b))
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if n == 0:
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return 0.0
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a, b = a[:n], b[:n]
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d = np.linalg.norm(a) * np.linalg.norm(b)
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return float(np.dot(a, b) / d) if d > 1e-10 else 0.0
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def cos(a, b, shape_a=None, shape_b=None):
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if shape_a and shape_b and len(shape_a) == 2 and len(shape_b) == 2:
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if shape_a[0] == shape_b[1] and shape_a[1] == shape_b[0]:
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ra = a.reshape(shape_a)
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rb = b.reshape(shape_b)
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c_normal = _cos_flat(ra.flatten(), rb.flatten())
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c_transposed = _cos_flat(ra.T.flatten(), rb.flatten())
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if c_transposed > c_normal:
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return c_transposed
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return c_normal
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return _cos_flat(a, b)
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def stft_cos(a, b, win=2048, hop=512):
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n = min(len(a), len(b))
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a, b = a[:n], b[:n]
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window = np.hanning(win)
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frames = (n - win) // hop + 1
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sa = np.zeros((frames, win // 2 + 1))
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sb = np.zeros((frames, win // 2 + 1))
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for i in range(frames):
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s = i * hop
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sa[i] = np.abs(np.fft.rfft(a[s:s+win] * window))
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sb[i] = np.abs(np.fft.rfft(b[s:s+win] * window))
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return _cos_flat(sa.flatten(), sb.flatten())
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def codes_to_python_format(codes_csv):
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"""Convert '43316,18426,...' to '<|audio_code_43316|><|audio_code_18426|>...'"""
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if not codes_csv:
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return ""
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return "".join(f"<|audio_code_{c.strip()}|>" for c in codes_csv.split(",") if c.strip())
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# GGML runner
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def run_ggml(dump_dir, req, cfg, gguf_path, adapter_dir=None):
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ggml_bin = "../build/ace-synth"
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if not os.path.isfile(ggml_bin):
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print(f"[GGML] binary not found: {ggml_bin}")
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return False
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os.makedirs(dump_dir, exist_ok=True)
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# Build request from input, override mode-specific params
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merged = dict(req)
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merged["seed"] = SEED
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merged["inference_steps"] = cfg["steps"]
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merged["guidance_scale"] = cfg["guidance"]
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merged["shift"] = cfg["shift"]
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merged["thinking"] = False
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request_json = os.path.join(dump_dir, "request0.json")
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with open(request_json, "w") as f:
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json.dump(merged, f, indent=4)
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cmd = [
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ggml_bin,
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"--dit", gguf_path,
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"--embedding", "../models/Qwen3-Embedding-0.6B-BF16.gguf",
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"--vae", "../models/vae-BF16.gguf",
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"--request", request_json,
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"--dump", dump_dir,
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]
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if adapter_dir:
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cmd += ["--adapter", adapter_dir]
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print(f"[GGML] Running {os.path.basename(gguf_path)}...")
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r = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=None, text=True)
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n = len([f for f in os.listdir(dump_dir) if f.endswith(".bin")])
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if r.returncode != 0:
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if n > 0:
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print(f"[GGML] WARNING: exit {r.returncode} but {n} dump files exist, continuing")
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else:
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print(f"[GGML] FAILED (exit {r.returncode})")
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if r.stdout:
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print(r.stdout[-500:])
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return False
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print(f"[GGML] Done, {n} dump files")
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return True
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# Python runner
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def run_python(dump_dir, req, cfg, adapter_dir=None):
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sys.path.insert(0, "../../ACE-Step-1.5")
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from acestep.handler import AceStepHandler
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os.makedirs(dump_dir, exist_ok=True)
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has_cfg = cfg["guidance"] > 1.0
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caption = req["caption"]
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lyrics = req.get("lyrics", "")
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bpm = req.get("bpm", 0)
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duration = req["duration"]
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language = req.get("vocal_language", "en")
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print(f"[Python] Initializing {cfg['config_path']}...")
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handler = AceStepHandler()
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handler.initialize_service(
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project_root="..",
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config_path=cfg["config_path"],
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device="cuda",
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)
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if adapter_dir:
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# torch.nn forbids '.' in module names, PEFT derives the adapter name
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# from the directory basename. Sanitize so directory names like
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# 'ACE-Step-v1.5-chinese-new-year-LoRA' do not abort Python ref load.
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adapter_name = os.path.basename(os.path.normpath(adapter_dir)).replace(".", "_") or "default"
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lr = handler.add_lora(adapter_dir, adapter_name=adapter_name)
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print(f"[Python] LoRA: {lr}")
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model = handler.model
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_dumps = {}
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orig_text = handler.infer_text_embeddings
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def hooked_text(*a, **kw):
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r = orig_text(*a, **kw)
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_dumps["text_hidden"] = r[0].clone()
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return r
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handler.infer_text_embeddings = hooked_text
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orig_lyric = handler.infer_lyric_embeddings
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def hooked_lyric(*a, **kw):
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r = orig_lyric(*a, **kw)
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_dumps["lyric_embed"] = r[0].clone()
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return r
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handler.infer_lyric_embeddings = hooked_lyric
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orig_cond = model.prepare_condition
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def hooked_prepare(*a, **kw):
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r = orig_cond(*a, **kw)
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enc_hs, enc_mask, ctx = r
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_dumps["enc_hidden"] = enc_hs[0].clone()
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_dumps["context"] = ctx[0].clone()
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if has_cfg:
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null_expanded = model.null_condition_emb.expand_as(enc_hs)
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_dumps["null_enc_hidden"] = null_expanded[0].clone()
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return r
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model.prepare_condition = hooked_prepare
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orig_noise = model.prepare_noise
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def hooked_noise(*a, **kw):
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n = orig_noise(*a, **kw)
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_dumps["noise"] = n[0].clone()
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return n
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model.prepare_noise = hooked_noise
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decoder = model.decoder
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_step = [0]
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orig_fwd = decoder.forward
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def hooked_fwd(*args, **kwargs):
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xt_in = args[0] if args else kwargs.get('hidden_states')
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step = _step[0]
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if step > 0 and xt_in is not None:
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_dumps[f"dit_step{step - 1}_xt"] = xt_in[0].clone()
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out = orig_fwd(*args, **kwargs)
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vt = out[0]
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if has_cfg and vt.shape[0] == 2:
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_dumps[f"dit_step{step}_vt_cond"] = vt[0].clone()
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_dumps[f"dit_step{step}_vt_uncond"] = vt[1].clone()
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else:
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_dumps[f"dit_step{step}_vt_cond"] = vt[0].clone()
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if not has_cfg:
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_dumps[f"dit_step{step}_vt"] = vt[0].clone()
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_step[0] += 1
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return out
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decoder.forward = hooked_fwd
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if has_cfg:
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gen_globals = model.generate_audio.__func__.__globals__
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_apg_step = [0]
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orig_apg = gen_globals['apg_forward']
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def hooked_apg(*args, **kwargs):
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result = orig_apg(*args, **kwargs)
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_dumps[f"dit_step{_apg_step[0]}_vt"] = result[0].clone()
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_apg_step[0] += 1
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return result
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gen_globals['apg_forward'] = hooked_apg
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_dumps["null_condition_emb"] = model.null_condition_emb.squeeze().clone()
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_hooks = []
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def make_hook(name, step_filter=0):
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def hook(module, input, output):
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if _step[0] == step_filter:
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out = output[0] if isinstance(output, tuple) else output
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_dumps[name] = out[0].clone().float()
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return hook
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_hooks.append(decoder.proj_in.register_forward_hook(make_hook("hidden_after_proj_in")))
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_hooks.append(decoder.condition_embedder.register_forward_hook(make_hook("enc_after_cond_emb")))
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_hooks.append(decoder.layers[0].register_forward_hook(make_hook("hidden_after_layer0")))
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_hooks.append(decoder.layers[0].self_attn.register_forward_hook(make_hook("layer0_sa_output")))
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for li in [6, 12, 18, cfg["n_layers"] - 1]:
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_hooks.append(decoder.layers[li].register_forward_hook(make_hook(f"hidden_after_layer{li}")))
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_hooks.append(decoder.time_embed.register_forward_hook(make_hook("temb_t")))
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# Hook detokenizer (runs during prepare_condition, before diffusion)
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if hasattr(model, 'detokenizer'):
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def detok_hook(module, input, output):
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_dumps["detok_output"] = output[0].clone().float()
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_hooks.append(model.detokenizer.register_forward_hook(detok_hook))
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gen_kwargs = dict(
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captions=caption, lyrics=lyrics, bpm=bpm,
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audio_duration=float(duration), seed=str(SEED),
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use_random_seed=False, batch_size=1,
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inference_steps=cfg["steps"], shift=cfg["shift"],
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guidance_scale=cfg["guidance"],
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infer_method="ode", vocal_language=language,
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audio_code_string=codes_to_python_format(req.get("audio_codes", "")),
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key_scale=req.get("keyscale", ""),
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time_signature=req.get("timesignature", ""),
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)
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# When audio_codes are present, Python auto-sets is_covers=True via
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# conditioning_masks.py (instruction match + has_code_hint).
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# This makes it use decoded codes as context, matching C++ behavior.
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# Do NOT patch is_covers to False, that would use silence instead of codes.
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tag = f"{cfg['config_path']}, {cfg['steps']} steps"
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if has_cfg:
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tag += f", CFG {cfg['guidance']}"
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print(f"[Python] Generating ({tag})...")
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result = handler.generate_music(**gen_kwargs)
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if not result.get("success"):
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print(f"[Python] Generation failed: {result.get('error', 'unknown')}")
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return False
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for h in _hooks:
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h.remove()
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extra = result.get("extra_outputs", {})
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if extra.get("pred_latents") is not None:
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_dumps["dit_x0"] = extra["pred_latents"][0]
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audios = result.get("audios", [])
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if audios and "tensor" in audios[0]:
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_dumps["vae_audio"] = audios[0]["tensor"].squeeze(0)
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audio_np = audios[0]["tensor"].squeeze(0).cpu().numpy()
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wav_path = os.path.join(dump_dir, "output.wav")
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import wave
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n_samples = audio_np.shape[1]
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interleaved = np.empty(2 * n_samples, dtype=np.float32)
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interleaved[0::2] = audio_np[0]
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interleaved[1::2] = audio_np[1]
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pcm = (np.clip(interleaved, -1, 1) * 32767).astype(np.int16)
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with wave.open(wav_path, 'w') as wf:
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wf.setnchannels(2)
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wf.setsampwidth(2)
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wf.setframerate(48000)
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wf.writeframes(pcm.tobytes())
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print(f"[Python] Wrote {wav_path}: {n_samples} samples ({n_samples/48000:.2f}s @ 48kHz stereo)")
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for name, tensor in sorted(_dumps.items()):
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save_dump(os.path.join(dump_dir, f"{name}.bin"), tensor)
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print(f"[Python] Done, {len(_dumps)} dump files")
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return True
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# comparison
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def build_stages(cfg):
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has_cfg = cfg["guidance"] > 1.0
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steps = cfg["steps"]
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stages = [
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"text_hidden", "lyric_embed", "enc_hidden", "detok_output", "context", "noise",
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"temb_t", "hidden_after_proj_in", "enc_after_cond_emb",
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"layer0_sa_output", "hidden_after_layer0",
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"hidden_after_layer6", "hidden_after_layer12", "hidden_after_layer18",
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f"hidden_after_layer{cfg['n_layers'] - 1}",
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]
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if has_cfg:
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stages += ["null_condition_emb", "null_enc_hidden"]
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if steps <= 8:
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step_indices = list(range(steps))
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else:
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step_indices = list(range(0, steps, 5))
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if (steps - 1) not in step_indices:
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step_indices.append(steps - 1)
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for si in step_indices:
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if has_cfg:
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stages.append(f"dit_step{si}_vt_cond")
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if si < 2:
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stages.append(f"dit_step{si}_vt_uncond")
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stages.append(f"dit_step{si}_vt")
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if si < steps - 1:
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stages.append(f"dit_step{si}_xt")
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stages += ["dit_x0", "vae_audio"]
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return stages
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def compare(dirs, stages, tag):
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labels = sorted(dirs.keys())
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pairs = [(labels[i], labels[j]) for i in range(len(labels)) for j in range(i+1, len(labels))]
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print(f"[{tag}] Cosine similarities GGML vs Python")
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print(f" {'stage':30s}", end="")
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for a, b in pairs:
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print(f" {a+' vs '+b:>14s}", end="")
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print()
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for stage in stages:
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data = {}
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for label, d in dirs.items():
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f = os.path.join(d, stage + ".bin")
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if os.path.isfile(f):
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data[label] = load_dump(f)
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if not data:
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continue
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print(f" {stage:30s}", end="")
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for a, b in pairs:
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if a in data and b in data:
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da, sa = data[a]
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db, sb = data[b]
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c = cos(da, db, sa, sb)
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print(f" {c:>14.6f}", end="")
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else:
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print(f" {'N/A':>14s}", end="")
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print()
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||||
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vae_data = {}
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for label, d in dirs.items():
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f = os.path.join(d, "vae_audio.bin")
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if os.path.isfile(f):
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vae_data[label] = load_dump(f)
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if len(vae_data) >= 2:
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print(f" {'vae_audio (STFT cosine)':30s}", end="")
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for a, b in pairs:
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if a in vae_data and b in vae_data:
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c = stft_cos(vae_data[a][0], vae_data[b][0])
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print(f" {c:>14.6f}", end="")
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else:
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print(f" {'N/A':>14s}", end="")
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print()
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||||
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||||
if len(pairs) > 0:
|
||||
a_label, b_label = pairs[0]
|
||||
a_dir, b_dir = dirs[a_label], dirs[b_label]
|
||||
xt_stages = [s for s in stages if "_xt" in s]
|
||||
if xt_stages:
|
||||
print(f"[{tag}] Error growth GGML vs Python")
|
||||
print(f" {'stage':22s} {'cos':>10s} {'max_err':>10s} {'mean_err':>10s}"
|
||||
f" {'mean_A':>10s} {'std_A':>10s} {'mean_B':>10s} {'std_B':>10s}")
|
||||
for stage in xt_stages:
|
||||
fa = os.path.join(a_dir, stage + ".bin")
|
||||
fb = os.path.join(b_dir, stage + ".bin")
|
||||
if os.path.isfile(fa) and os.path.isfile(fb):
|
||||
da_raw, sa = load_dump(fa)
|
||||
db_raw, sb = load_dump(fb)
|
||||
if len(sa) == 2 and len(sb) == 2 and sa[0] == sb[0] and sa[1] == sb[1]:
|
||||
da = da_raw.reshape(sa)
|
||||
db = db_raw.reshape(sb)
|
||||
c_flat = _cos_flat(da.flatten(), db.flatten())
|
||||
c_trans = _cos_flat(da.T.flatten(), db.flatten())
|
||||
if c_trans > c_flat:
|
||||
da = da.T
|
||||
da, db = da.flatten(), db.flatten()
|
||||
else:
|
||||
da, db = da_raw, db_raw
|
||||
n = min(len(da), len(db))
|
||||
da, db = da[:n], db[:n]
|
||||
c = _cos_flat(da, db)
|
||||
diff = np.abs(da - db)
|
||||
print(f" {stage:22s} {c:10.6f} {diff.max():10.6f} {diff.mean():10.6f}"
|
||||
f" {da.mean():10.6f} {da.std():10.6f} {db.mean():10.6f} {db.std():10.6f}")
|
||||
else:
|
||||
missing = []
|
||||
if not os.path.isfile(fa): missing.append(a_label)
|
||||
if not os.path.isfile(fb): missing.append(b_label)
|
||||
print(f" {stage:22s} missing: {', '.join(missing)}")
|
||||
|
||||
# main
|
||||
|
||||
def run_mode(mode_name, cfg, req, gguf_path, adapter_dir=None):
|
||||
dump_ggml = f"ggml-{mode_name}"
|
||||
dump_python = f"python-{mode_name}"
|
||||
|
||||
tag = mode_name.upper() if mode_name == "sft" else mode_name.capitalize()
|
||||
cfg_str = f"steps={cfg['steps']}, shift={cfg['shift']}"
|
||||
if cfg['guidance'] > 1.0:
|
||||
cfg_str += f", CFG={cfg['guidance']}"
|
||||
print(f"[{tag}] {cfg_str} | {os.path.basename(gguf_path)}")
|
||||
|
||||
if os.path.isdir(dump_ggml):
|
||||
shutil.rmtree(dump_ggml)
|
||||
if not run_ggml(dump_ggml, req, cfg, gguf_path, adapter_dir):
|
||||
print(f"[{tag}] GGML failed")
|
||||
return False
|
||||
|
||||
if os.path.isdir(dump_python):
|
||||
shutil.rmtree(dump_python)
|
||||
if not run_python(dump_python, req, cfg, adapter_dir):
|
||||
print(f"[{tag}] Python failed")
|
||||
return False
|
||||
|
||||
stages = build_stages(cfg)
|
||||
compare({"GGML": dump_ggml, "Python": dump_python}, stages, tag)
|
||||
return True
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description="GGML vs Python cosine similarity comparison")
|
||||
ap.add_argument("--mode", default="turbo", choices=list(MODE_CONFIG.keys()) + ["all"],
|
||||
help="which model to test (default: turbo)")
|
||||
ap.add_argument("--quant", default="BF16",
|
||||
help="quantization suffix for GGUF (default: BF16, e.g. Q6_K, Q8_0)")
|
||||
ap.add_argument("--adapter", default=None,
|
||||
help="path to adapter directory (optional)")
|
||||
args = ap.parse_args()
|
||||
|
||||
req = load_request()
|
||||
|
||||
modes = list(MODE_CONFIG.keys()) if args.mode == "all" else [args.mode]
|
||||
ok = True
|
||||
for m in modes:
|
||||
cfg = MODE_CONFIG[m]
|
||||
gguf_path = f"../models/{cfg['gguf_base']}-{args.quant}.gguf"
|
||||
if not os.path.isfile(gguf_path):
|
||||
print(f"[Error] GGUF not found: {gguf_path}")
|
||||
ok = False
|
||||
continue
|
||||
if not run_mode(m, cfg, req, gguf_path, args.adapter):
|
||||
ok = False
|
||||
|
||||
return 0 if ok else 1
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user