app.py

# app.py ──────────────────────────────────────────────────────────────
import os, json, torch, asyncio
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM, LogitsProcessor, DynamicCache # Added StaticCache
from snac import SNAC

# 0) Login + Device ---------------------------------------------------
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
    login(HF_TOKEN)

device = "cuda" if torch.cuda.is_available() else "cpu"
torch.backends.cuda.enable_flash_sdp(False)          # PyTorch‑2.2‑Bug

# 1) Konstanten -------------------------------------------------------
REPO           = "SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1"
CHUNK_TOKENS   = 50
START_TOKEN    = 128259
NEW_BLOCK      = 128257
EOS_TOKEN      = 128258
AUDIO_BASE     = 128266
AUDIO_SPAN   = 4096 * 7                                # 28 672 Codes
AUDIO_IDS  = torch.arange(AUDIO_BASE, AUDIO_BASE + AUDIO_SPAN) # Renamed VALID_AUDIO to AUDIO_IDS

# 2) Logit‑Mask (NEW_BLOCK + Audio; EOS erst nach 1. Block) ----------
class AudioMask(LogitsProcessor):
    def __init__(self, audio_ids: torch.Tensor):
        super().__init__()
        self.allow = torch.cat([
            torch.tensor([NEW_BLOCK], device=audio_ids.device),
            audio_ids
        ])
        self.eos   = torch.tensor([EOS_TOKEN], device=audio_ids.device)
        self.sent_blocks = 0
        self.buffer_pos = 0 # Added buffer position

    def __call__(self, input_ids, scores):
        allow = torch.cat([self.allow, self.eos]) # Reverted masking logic
        mask = torch.full_like(scores, float("-inf"))
        mask[:, allow] = 0
        return scores + mask

# 3) FastAPI Grundgerüst ---------------------------------------------
app = FastAPI()

@app.get("/")
def hello():
    return {"status": "ok"}

@app.on_event("startup")
def load_models():
    global tok, model, snac, masker
    print("⏳ Lade Modelle …", flush=True)

    tok   = AutoTokenizer.from_pretrained(REPO)
    snac  = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(device)
    model = AutoModelForCausalLM.from_pretrained(
        REPO,
        device_map={"": 0} if device == "cuda" else None,
        torch_dtype=torch.bfloat16 if device == "cuda" else None,
        low_cpu_mem_usage=True,
    )
    model.config.pad_token_id = model.config.eos_token_id
    masker = AudioMask(AUDIO_IDS.to(device))

    print("✅ Modelle geladen", flush=True)

# 4) Helper -----------------------------------------------------------
def build_prompt(text: str, voice: str):
    prompt_ids = tok(f"{voice}: {text}", return_tensors="pt").input_ids.to(device)
    ids   = torch.cat([torch.tensor([[START_TOKEN]], device=device),
                       prompt_ids,
                       torch.tensor([[128009, 128260]], device=device)], 1)
    attn  = torch.ones_like(ids)
    return ids, attn # Ensure attention mask is created

def decode_block(block7: list[int]) -> bytes:
    l1,l2,l3=[],[],[]
    l1.append(block7[0] - 0 * 4096) # Subtract position 0 offset
    l2.append(block7[1] - 1 * 4096) # Subtract position 1 offset
    l3 += [block7[2] - 2 * 4096, block7[3] - 3 * 4096] # Subtract position offsets
    l2.append(block7[4] - 4 * 4096) # Subtract position 4 offset
    l3 += [block7[5] - 5 * 4096, block7[6] - 6 * 4096] # Subtract position offsets

    with torch.no_grad():
        codes = [torch.tensor(x, device=device).unsqueeze(0)
                 for x in (l1,l2,l3)]
        audio = snac.decode(codes).squeeze().detach().cpu().numpy()

    return (audio*32767).astype("int16").tobytes()

# 5) WebSocket‑Endpoint ----------------------------------------------
@app.websocket("/ws/tts")
async def tts(ws: WebSocket):
    await ws.accept()
    try:
        req   = json.loads(await ws.receive_text())
        text  = req.get("text", "")
        voice = req.get("voice", "Jakob")

        ids, attn  = build_prompt(text, voice)
        past       = None # Reverted past initialization
        offset_len = ids.size(1)          # wie viele Tokens existieren schon
        last_tok   = None # Initialized last_tok
        buf         = []
        past_key_values = DynamicCache()
        while True:
            print(f"DEBUG: Before generate - past is None: {past is None}", flush=True) # Added logging
            print(f"DEBUG: Before generate - type of past: {type(past) if past is not None else 'None'}", flush=True) # Added logging
            # --- Mini‑Generate (StaticCache via cache_implementation) -------------------------------------------
            gen = model.generate(
                input_ids       = ids if past is None else torch.tensor([[last_tok]], device=device), # Use past is None check
                attention_mask  = attn if past is None else None, # Use past is None check
                past_key_values = past_key_values, # Pass past (will be None initially, then the cache object)
                max_new_tokens = 1, # Set max_new_tokens to 1 for debugging cache
                logits_processor=[masker],
                do_sample=True, temperature=0.7, top_p=0.95,
                use_cache=True, # Re-enabled cache
                return_dict_in_generate=True,
                #return_legacy_cache=True,
                #cache_implementation="static" # Enabled StaticCache via implementation
            )
            print(f"DEBUG: After generate - type of gen.past_key_values: {type(gen.past_key_values)}", flush=True) # Added logging

            # ----- neue Tokens heraus schneiden --------------------------
            seq  = gen.sequences[0].tolist()
            new  = seq[offset_len:]
            if not new:                         # nichts -> fertig
                break
            offset_len += len(new)

            # ----- Update past and last_tok (Cache Re-enabled) ---------
            # ids = torch.tensor([seq], device=device) # Removed full sequence update
            # attn = torch.ones_like(ids) # Removed full sequence update
            #pkv = gen.past_key_values # Update past with the cache object returned by generate
            print(f"DEBUG: After cache update - type of past: {type(past)}", flush=True) # Added logging
            #if isinstance(pkv, StaticCache): pkv = pkv.to_legacy()
            past = gen.past_key_values
            print(f"DEBUG: After cache update - type of past: {type(past)}", flush=True) # Added logging

            last_tok = new[-1]

            print("new tokens:", new[:25], flush=True)

            # ----- Token‑Handling ----------------------------------------
            for t in new:
                if t == EOS_TOKEN: # Re-enabled EOS check
                    raise StopIteration # Re-enabled EOS check
                if t == NEW_BLOCK:
                    buf.clear()
                    continue
                buf.append(t - AUDIO_BASE) # Reverted to appending relative token
                if len(buf) == 7:
                    await ws.send_bytes(decode_block(buf))
                    buf.clear()
                    masker.sent_blocks = 1      # ab jetzt EOS zulässig

    except (StopIteration, WebSocketDisconnect):
        pass
    except Exception as e:
        print("❌ WS‑Error:", e, flush=True)
        import traceback
        traceback.print_exc()
        if ws.client_state.name != "DISCONNECTED":
            await ws.close(code=1011)
    finally:
        if ws.client_state.name != "DISCONNECTED":
            try:
                await ws.close()
            except RuntimeError:
                pass

# 6) Dev‑Start --------------------------------------------------------
if __name__ == "__main__":
    import uvicorn, sys
    uvicorn.run("app:app", host="0.0.0.0", port=7860, log_level="info")