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ebaa263 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | #!/usr/bin/env python3
"""Verify the exported ONNX graphs against the original torch model.
1. Encoder parity : ONNX encoder vs torch EncoderForExport (max abs diff).
2. Decoder parity : ONNX decoder step-0 logits vs torch DecoderMergedForExport.
3. End-to-end : greedy decode via ONNX (Rust prompt) -> text; compare to model.generate().
Usage: python verify.py [sample.wav] [encoder_suffix] [decoder_suffix]
python verify.py sample_en.wav "" "" # fp32
python verify.py sample_en.wav "_q4" "_q4" # q4
"""
import sys
import numpy as np
import onnxruntime as ort
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from export_merged import EncoderForExport, DecoderMergedForExport, NUM_DECODER_LAYERS
MODEL_DIR = "model_ar"
WAV = sys.argv[1] if len(sys.argv) > 1 else "sample_en.wav"
ENC_SFX = sys.argv[2] if len(sys.argv) > 2 else ""
DEC_SFX = sys.argv[3] if len(sys.argv) > 3 else ""
# Rust build_prompt(language="ar", punctuation=True) -> token ids
PROMPT_TOKENS = ["▁", "<|startofcontext|>", "<|startoftranscript|>", "<|emo:undefined|>",
"<|ar|>", "<|ar|>", "<|pnc|>", "<|noitn|>", "<|notimestamp|>", "<|nodiarize|>"]
EOS_ID = 3
H, D = 8, 128
def sess(path):
return ort.InferenceSession(path, providers=["CPUExecutionProvider"])
def main():
import soundfile as sf
print(f"Loading torch model + processor from {MODEL_DIR}")
model = AutoModelForSpeechSeq2Seq.from_pretrained(
MODEL_DIR, trust_remote_code=True, dtype=torch.float32).eval()
proc = AutoProcessor.from_pretrained(MODEL_DIR, trust_remote_code=True)
vocab = {t: i for t, i in __import__("json").load(
open(f"{MODEL_DIR}/tokenizer.json", encoding="utf-8"))["model"]["vocab"].items()}
id2tok = {i: t for t, i in vocab.items()}
prompt_ids = [vocab[t] for t in PROMPT_TOKENS]
print("prompt ids:", prompt_ids)
# Build the featurizer from config.preprocessor (preprocessor_config.json lacks params, so the
# AutoProcessor falls back to nfilt=64 — wrong; the real params live in config.json).
FE = type(proc.feature_extractor) # CohereAsrFeatureExtractor (already trust_remote_code-loaded)
pp = model.config.preprocessor
fe = FE(
feature_size=pp["features"], sampling_rate=pp["sample_rate"],
n_window_size=int(pp["window_size"] * pp["sample_rate"]),
n_window_stride=int(pp["window_stride"] * pp["sample_rate"]),
window=pp["window"], normalize=pp["normalize"], n_fft=pp["n_fft"],
log=pp["log"], dither=pp["dither"], pad_to=pp["pad_to"], mel_norm="slaney",
)
wav, sr = sf.read(WAV)
if wav.ndim > 1:
wav = wav.mean(axis=1)
wav = wav.astype(np.float32)
feats = fe(wav, sampling_rate=16000, return_tensors="pt")
input_features = feats["input_features"] # (B, 128, T) -> (B, T, 128)
if input_features.shape[-1] != 128:
input_features = input_features.transpose(1, 2)
print("input_features:", tuple(input_features.shape))
# ---- 1. encoder parity ----
with torch.no_grad():
torch_enc = EncoderForExport(model).eval()(input_features)
enc_sess = sess(f"onnx/encoder_model{ENC_SFX}.onnx")
onnx_enc = enc_sess.run(["last_hidden_state"],
{"input_features": input_features.numpy()})[0]
diff = np.abs(torch_enc.numpy() - onnx_enc).max()
print(f"[encoder] shape {onnx_enc.shape} max|torch-onnx| = {diff:.3e}")
# ---- 2. decoder step-0 parity ----
dec_sess = sess(f"onnx/decoder_model_merged{DEC_SFX}.onnx")
P = len(prompt_ids)
empties = {}
feeds = {
"input_ids": np.array([prompt_ids], np.int64),
"attention_mask": np.ones((1, P), np.int64),
"position_ids": np.arange(P, dtype=np.int64)[None],
"num_logits_to_keep": np.array(1, np.int64),
"encoder_hidden_states": onnx_enc,
}
for i in range(NUM_DECODER_LAYERS):
for kind in ("decoder", "encoder"):
for kv in ("key", "value"):
feeds[f"past_key_values.{i}.{kind}.{kv}"] = np.zeros((1, H, 0, D), np.float32)
onnx_logits = dec_sess.run(["logits"], feeds)[0]
with torch.no_grad():
args = (torch.tensor([prompt_ids]), torch.ones(1, P, dtype=torch.long),
torch.arange(P)[None], torch.tensor(1),
torch.tensor(onnx_enc))
past = []
for _ in range(NUM_DECODER_LAYERS):
past += [torch.zeros(1, H, 0, D), torch.zeros(1, H, 0, D),
torch.zeros(1, H, 0, D), torch.zeros(1, H, 0, D)]
torch_logits = DecoderMergedForExport(model).eval()(*args, *past)[0]
dl = np.abs(torch_logits.numpy() - onnx_logits).max()
print(f"[decoder] logits {onnx_logits.shape} max|torch-onnx| = {dl:.3e}")
# ---- 3. end-to-end greedy decode via ONNX ----
def decode_text(ids):
out, buf = "", bytearray()
for tid in ids:
tok = id2tok.get(tid, "")
if tok.startswith("<0x") and tok.endswith(">"):
buf.append(int(tok[3:-1], 16)); continue
if buf:
out += buf.decode("utf-8", "replace"); buf = bytearray()
if tok.startswith("<|") or tok in ("<s>", "</s>"):
continue
out += tok.replace("▁", " ")
if buf:
out += buf.decode("utf-8", "replace")
return out.strip()
past = {f"past_key_values.{i}.{k}.{kv}": np.zeros((1, H, 0, D), np.float32)
for i in range(NUM_DECODER_LAYERS) for k in ("decoder", "encoder") for kv in ("key", "value")}
cur = prompt_ids[:]
gen = []
for step in range(448):
L = len(cur)
attn = len(prompt_ids) + step
pos = (np.arange(L) if step == 0 else np.array([attn - 1])).astype(np.int64)[None]
feeds = {"input_ids": np.array([cur], np.int64),
"attention_mask": np.ones((1, attn if step else L), np.int64),
"position_ids": pos, "num_logits_to_keep": np.array(1, np.int64),
"encoder_hidden_states": onnx_enc, **past}
outs = dec_sess.run(None, feeds)
names = [o.name for o in dec_sess.get_outputs()]
res = dict(zip(names, outs))
nxt = int(res["logits"][0, -1].argmax())
if nxt == EOS_ID:
break
gen.append(nxt)
for i in range(NUM_DECODER_LAYERS):
for k in ("decoder", "encoder"):
for kv in ("key", "value"):
v = res[f"present.{i}.{k}.{kv}"]
if v.shape[2] != 0:
past[f"past_key_values.{i}.{k}.{kv}"] = v
cur = [nxt]
print(f"\n[ONNX greedy] ({len(gen)} tok): {decode_text(gen)!r}")
# ---- reference: model.generate ----
try:
with torch.no_grad():
g = model.generate(input_features=feats["input_features"], max_new_tokens=200)
txt = proc.batch_decode(g, skip_special_tokens=True)[0] if hasattr(proc, "batch_decode") \
else proc.tokenizer.decode(g[0], skip_special_tokens=True)
print("[generate] ", txt.strip()[:200])
except Exception as e:
print("[generate] skipped:", type(e).__name__, str(e)[:160])
if __name__ == "__main__":
main()
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