Fabio
c36510c21e
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49 lines
2.2 KiB
Python
49 lines
2.2 KiB
Python
# Dump backbone parity fixtures for MOSS-TTS-v1.5, in the schema the existing
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# moss_voicegen backbone_parity harness already reads.
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import sys, json, torch
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from transformers import AutoModel, AutoProcessor
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MODEL="/mnt/data/models/MOSS-TTS-v1.5"; CODEC="/mnt/data/models/MOSS-Audio-Tokenizer"
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OUT="/home/chris/Programming/audio.cpp-fork/tests/moss_tts_v15/reference"
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import os; os.makedirs(OUT, exist_ok=True)
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dev="cuda"
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proc=AutoProcessor.from_pretrained(MODEL, trust_remote_code=True, codec_path=CODEC)
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model=AutoModel.from_pretrained(MODEL, trust_remote_code=True, dtype=torch.bfloat16).eval().to(dev)
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pm=sys.modules[type(proc).__module__]
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NAME="en_instruction"
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TEXT="The quick brown fox jumps over the lazy dog, and then says hello."
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INSTR="A warm, friendly female voice speaking clearly at a natural pace."
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msg=pm.UserMessage(text=TEXT, instruction=INSTR)
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ids=proc([msg],mode="generation")["input_ids"] # (1, rows, 1+n_vq)
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rows=ids.shape[1]; nvq=ids.shape[2]-1
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print(f"prompt rows={rows} channels={ids.shape[2]} (1+{nvq})", flush=True)
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with torch.no_grad():
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out=model(input_ids=ids.to(dev), attention_mask=torch.ones(ids.shape[:2],dtype=torch.bool,device=dev))
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# hidden_states[-1] is the post-final-norm state the heads consume, which is what
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# the C++ prefill() returns.
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hid=out.hidden_states[-1][0].float().cpu() # (rows, hidden)
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hidden_size=hid.shape[1]
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print(f"hidden: {tuple(hid.shape)}", flush=True)
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probe=[0, rows//2, rows-1]
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# Record the fields as the message actually carried them: writing a value the
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# message did not use makes the fixture describe a prompt nobody rendered.
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json.dump({
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"name": NAME,
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"text": msg.text,
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"instruction": msg.instruction,
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"language": msg.language,
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"tokens": msg.tokens,
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"content": msg._content,
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"input_ids": ids[0].tolist(),
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}, open(f"{OUT}/ref_prompt_{NAME}.json","w"))
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json.dump({
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"name": NAME, "rows": rows, "hidden_size": hidden_size,
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"last_hidden": hid[-1].tolist(),
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"last_hidden_abs_max": float(hid[-1].abs().max()),
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"probe_positions": probe,
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"probe_hidden": [hid[p].tolist() for p in probe],
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}, open(f"{OUT}/ref_hidden_{NAME}.json","w"))
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print(f"wrote {OUT}/ref_prompt_{NAME}.json and ref_hidden_{NAME}.json")
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print(f"last_hidden abs_max={float(hid[-1].abs().max()):.4f}")
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