d68330080b
The Vulkan-ordinal GGUF preflight downloads before the Phase 2 block, so it now takes local_files_only and the forced-offline wrap; both download blocks force offline so the cache-size verification (get_paths_info) stays off the network. The offline helper now overrides an explicitly falsy HF_HUB_OFFLINE=0 under force and restores prior values on exit. The validate route keeps its whole metadata and security preflight offline, not just the identifier probe. The python load path survives the process boundary: the orchestrator forwards the flag and the route-resolved snapshot path into the worker, which re-applies the path rewrite after rebuilding its ModelConfig, runs the entire load under a scoped offline env, and passes the flag to the vision processor fallback so a Hub base_model resolves from cache or fails over instead of downloading.
2535 lines
103 KiB
Python
2535 lines
103 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Core inference backend."""
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from unsloth import FastLanguageModel, FastVisionModel
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from unsloth.chat_templates import get_chat_template
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from transformers import TextIteratorStreamer, TextStreamer
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from peft import PeftModel, PeftModelForCausalLM
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import contextlib
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import json
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import sys
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import torch
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from pathlib import Path
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from typing import Optional, Union, Generator, Tuple
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from utils.models import ModelConfig, get_base_model_from_lora
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from utils.paths import is_model_cached
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from utils.transformers_dtype import dtype_kwargs
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from utils.utils import format_error_message
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from utils.hardware import (
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get_device,
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clear_gpu_cache,
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log_gpu_memory,
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get_device_map,
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raise_if_offloaded,
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get_visible_gpu_count,
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)
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from core.inference.audio_codecs import AudioCodecManager
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from core.inference.runtime_context import runtime_context_length
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from core.inference.message_content import content_to_text
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from core.inference.chat_eos import (
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chat_eos_repair,
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resolve_chat_turn_end_eos_ids_using,
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)
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from core.inference.chat_template_helpers import (
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ReasoningChannelNormalizer,
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detect_reasoning_channel_markers,
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detect_think_prefill,
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)
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from core.inference.presence_penalty import _make_presence_penalty_processor
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from io import StringIO
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import structlog
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from loggers import get_logger
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logger = get_logger(__name__)
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class HarmonyTextStreamer:
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"""Streaming text decoder for the gpt-oss harmony channel protocol.
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gpt-oss emits multi-channel output via ``<|channel|>analysis<|message|>...``
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/ ``<|channel|>final<|message|>...``. Plain skip_special_tokens streaming
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glues channel names to content. This decodes with skip_special_tokens=False
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and parses statefully: emit ``<think>`` on first analysis, stream analysis,
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emit ``</think>`` on first final, stream final. Tracking per-channel lengths
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avoids the delta-on-transformed bug where wrapping tags shift position.
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Same put/end/iterator interface as TextIteratorStreamer.
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"""
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import re as _re
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_HARMONY_RE = _re.compile(
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r"<\|channel\|>(\w+)<\|message\|>(.*?)(?=<\|end\|>|<\|channel\|>|\Z)",
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_re.DOTALL,
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)
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def __init__(
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self,
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tokenizer,
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*,
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skip_prompt: bool = True,
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timeout: float = 0.2,
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):
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import queue
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self.tokenizer = tokenizer
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self.skip_prompt = skip_prompt
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self.timeout = timeout
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self._queue: queue.Queue = queue.Queue()
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self._token_ids: list = []
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self._prompt_len: int = 0
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self._is_first_put: bool = True
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self._stop: bool = False
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# Stateful channel tracking avoids delta-on-transformed bugs
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self._emitted_think_open: bool = False
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self._emitted_think_close: bool = False
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self._analysis_emitted: int = 0 # chars of analysis content emitted
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self._final_emitted: int = 0 # chars of final content emitted
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# put / end — called from the generation thread
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def put(self, value):
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"""Receive new token IDs from model.generate()."""
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import torch
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if isinstance(value, torch.Tensor):
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# shape (batch, seq) — take first batch element
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ids = value[0].tolist() if value.dim() > 1 else value.tolist()
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elif isinstance(value, (list, tuple)):
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ids = list(value)
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else:
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ids = [value]
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if self._is_first_put and self.skip_prompt:
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# First call is the full prompt; remember its length.
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self._prompt_len = len(ids)
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self._token_ids = list(ids)
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self._is_first_put = False
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return
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self._token_ids.extend(ids)
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# Decode only the generated part (after the prompt).
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gen_ids = self._token_ids[self._prompt_len :]
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raw = self.tokenizer.decode(gen_ids, skip_special_tokens = False)
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self._process_incremental(raw)
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def end(self):
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"""Signal generation is complete."""
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# Final decode to capture remaining content.
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gen_ids = self._token_ids[self._prompt_len :]
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if gen_ids:
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raw = self.tokenizer.decode(gen_ids, skip_special_tokens = False)
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self._process_incremental(raw)
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# Close any open think tags.
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if self._emitted_think_open and not self._emitted_think_close:
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self._queue.put("</think>")
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self._emitted_think_close = True
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self._stop = True
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self._queue.put(None) # sentinel
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# Iterator interface — consumed by the streaming loop
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def __iter__(self):
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return self
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def __next__(self):
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from queue import Empty
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while True:
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try:
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val = self._queue.get(timeout = self.timeout)
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except Empty:
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if self._stop:
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raise StopIteration
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raise # propagate Empty so caller can check thread liveness
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if val is None:
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raise StopIteration
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return val
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# Stateful incremental harmony protocol parsing
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def _process_incremental(self, raw: str) -> None:
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"""Parse harmony channels and emit per-channel deltas (tracked by length, not whole-text diff)."""
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# If raw has <|channel|> but no complete channel+message pair yet, buffer.
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has_channel_token = "<|channel|>" in raw
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matches = list(self._HARMONY_RE.finditer(raw))
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if has_channel_token and not matches:
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# Partial harmony markup still building — wait for more tokens.
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return
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if not has_channel_token and not matches:
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return
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for m in matches:
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channel = m.group(1).lower()
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content = m.group(2)
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if channel == "analysis":
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if not self._emitted_think_open:
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self._queue.put("<think>")
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self._emitted_think_open = True
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new_content = content[self._analysis_emitted :]
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if new_content:
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self._analysis_emitted = len(content)
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self._queue.put(new_content)
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elif channel in ("final", "assistant"):
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if self._emitted_think_open and not self._emitted_think_close:
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self._queue.put("</think>")
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self._emitted_think_close = True
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new_content = content[self._final_emitted :]
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if new_content:
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self._final_emitted = len(content)
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self._queue.put(new_content)
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class ReasoningTextIteratorStreamer(TextIteratorStreamer):
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"""TextIteratorStreamer that preserves native channel tokens until parsed."""
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def __init__(
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self,
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tokenizer,
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*,
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markers: tuple[str, str],
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skip_prompt: bool = True,
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timeout: float = 0.2,
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cancel_event = None,
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**decode_kwargs,
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):
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decode_kwargs["skip_special_tokens"] = False
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super().__init__(tokenizer, skip_prompt = skip_prompt, timeout = timeout, **decode_kwargs)
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self._normalizer = ReasoningChannelNormalizer(*markers)
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self._cancel_event = cancel_event
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self._aborted = False
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def abort(self):
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"""Mark generation as failed so ``end`` drains without closing."""
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self._aborted = True
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def on_finalized_text(
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self,
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text: str,
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stream_end: bool = False,
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):
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"""Queue canonical deltas, closing only on natural stream completion."""
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delta = self._normalizer.feed(text)
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if delta:
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self.text_queue.put(delta, timeout = self.timeout)
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if stream_end:
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cancelled = self._aborted or (
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self._cancel_event is not None and self._cancel_event.is_set()
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)
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tail = self._normalizer.drain() if cancelled else self._normalizer.finish()
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if tail:
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self.text_queue.put(tail, timeout = self.timeout)
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self.text_queue.put(self.stop_signal, timeout = self.timeout)
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class _GenerationThreadError(RuntimeError):
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"""Generation worker failures that should propagate through stream routes."""
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class InferenceBackend:
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"""Unified inference backend supporting text, vision, and LoRA models"""
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def __init__(self):
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self.models = {}
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self.active_model_name = None
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self.loading_models = set()
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self.loaded_local_models = [] # [(display_name, path), ...]
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from core.inference.defaults import get_default_models
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self.default_models = get_default_models()
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self.device = get_device().value
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self._audio_codec_manager = AudioCodecManager()
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# _generation_lock serializes model.generate(). Plain Lock (NOT RLock):
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# RLock reentrancy would let concurrent compare-mode requests race on
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# the GPU. Acquired by the background generation thread, not the event-loop.
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import threading
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self._generation_lock = threading.Lock()
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self._model_state_lock = threading.Lock()
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logger.info(f"InferenceBackend initialized on {self.device}")
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@staticmethod
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def _normalize_top_k(top_k: int) -> int:
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# API uses -1 to disable top-k; transformers uses 0.
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return 0 if top_k < 0 else top_k
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def _resolve_chat_eos(self, model_name: str) -> None:
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"""Resolve this chat model's assistant-turn-end stop tokens once at load,
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cache them in model_info, and repair generation_config so every
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``.generate()`` path stops at the turn boundary.
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Some checkpoints (e.g. Qwen3.5 / Qwen3.6 small chat models) end turns with
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``<|im_end|>`` but ship ``config.eos_token_id = <|endoftext|>`` and no
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``generation_config.json``, so paths that read ``generation_config`` (the
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vision path, tool loops) run past the turn and loop. Turn-end markers are
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derived from the chat_template (see chat_eos.resolve_chat_turn_end_eos_ids),
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so base/coder models and harmony templates are left untouched.
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"""
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info = self.models.get(model_name) or {}
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model = info.get("model")
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container = info.get("tokenizer")
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tokenizer = getattr(container, "tokenizer", container) # unwrap processors
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if model is None or tokenizer is None:
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return
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# Vision models carry the chat_template on the processor, not the inner
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# tokenizer. Read markers from whichever has one, but resolve ids on the
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# generation tokenizer, else the vision path misses the turn-end token.
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template_source = container if getattr(container, "chat_template", None) else tokenizer
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try:
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turn_end_ids = resolve_chat_turn_end_eos_ids_using(template_source, tokenizer)
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except Exception as e: # never block a load on eos resolution
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logger.warning("Chat turn-end eos resolution failed for %s: %s", model_name, e)
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return
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info["chat_turn_end_eos_ids"] = turn_end_ids
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gen = getattr(model, "generation_config", None)
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if gen is None:
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return
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repaired = chat_eos_repair(gen.eos_token_id, turn_end_ids)
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if repaired is None:
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return
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previous = gen.eos_token_id
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gen.eos_token_id = repaired
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logger.info(
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"Repaired generation_config.eos_token_id for %s: %s -> %s",
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model_name,
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previous,
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repaired,
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)
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def load_model(
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self,
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config: ModelConfig,
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max_seq_length: int = 2048,
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dtype = None,
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load_in_4bit: bool = True,
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hf_token: Optional[str] = None,
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trust_remote_code: bool = False,
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gpu_ids: Optional[list[int]] = None,
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local_files_only: bool = False,
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) -> bool:
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"""Load any model: base, LoRA adapter, text, or vision."""
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# Keep the token so the native-template fallback can fetch a
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# gated model's repo template later during generation.
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self._hf_token = hf_token
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# GGUF uses max_seq_length=0 as "model default"; Unsloth crashes on it.
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if max_seq_length <= 0:
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max_seq_length = 2048
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try:
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model_name = config.identifier
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# Already loaded?
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if model_name in self.models and self.models[model_name].get("model"):
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logger.info(f"Model {model_name} already loaded")
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if hf_token:
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self.models[model_name]["hf_token"] = hf_token
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self.active_model_name = model_name
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return True
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# Currently loading?
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if model_name in self.loading_models:
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logger.info(f"Model {model_name} is already being loaded")
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return False
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self.loading_models.add(model_name)
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device_map = get_device_map(gpu_ids)
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logger.info(
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f"Using device_map='{device_map}' ({get_visible_gpu_count()} GPU(s) visible)"
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)
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self.models[model_name] = {
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# Per-model token: the native-template fallback must use the
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# token this model was loaded with, not whichever loaded last.
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"hf_token": hf_token,
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# Per-model consent: the native-template reload must re-use the
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# exact trust_remote_code this model (and a LoRA's base) was loaded
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# with, so a custom-code tokenizer repo can be re-fetched without
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# executing any code the user did not already consent to.
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"trust_remote_code": trust_remote_code,
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"is_vision": config.is_vision,
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"is_lora": config.is_lora,
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"is_audio": config.is_audio,
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"audio_type": config.audio_type,
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"has_audio_input": config.has_audio_input,
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"model_path": config.path,
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"base_model": config.base_model if config.is_lora else None,
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"loaded_adapters": {},
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"active_adapter": None,
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}
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# ── Audio model loading path ──────────────────────────
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if config.is_audio:
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audio_type = config.audio_type
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adapter_info = " (LoRA adapter)" if config.is_lora else ""
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logger.info(f"Loading audio ({audio_type}) model{adapter_info}: {model_name}")
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log_gpu_memory(f"Before loading {model_name}")
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if audio_type == "csm":
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from unsloth import FastModel
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from transformers import CsmForConditionalGeneration
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model, processor = FastModel.from_pretrained(
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config.path,
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auto_model = CsmForConditionalGeneration,
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load_in_4bit = False,
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device_map = device_map,
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token = hf_token if hf_token and hf_token.strip() else None,
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trust_remote_code = trust_remote_code,
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)
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FastModel.for_inference(model)
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self.models[model_name]["model"] = model
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self.models[model_name]["tokenizer"] = processor
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self.models[model_name]["processor"] = processor
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elif audio_type == "bicodec":
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import os
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from unsloth import FastModel
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if config.is_lora and config.base_model:
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# LoRA adapter: base_model is .../Spark-TTS-0.5B/LLM;
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# BiCodec weights live in the parent dir.
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base_path = config.base_model
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if os.path.isdir(base_path):
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abs_repo_path = os.path.abspath(os.path.dirname(base_path))
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else:
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# base_model is an HF ID — download it.
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from huggingface_hub import snapshot_download
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local_dir = base_path.split("/")[-1]
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repo_path = snapshot_download(base_path, local_dir = local_dir)
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abs_repo_path = os.path.abspath(repo_path)
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logger.info(
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f"Spark-TTS LoRA: loading adapter from {config.path}, BiCodec from {abs_repo_path}"
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)
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model, tokenizer = FastModel.from_pretrained(
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config.path,
|
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dtype = torch.float32,
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load_in_4bit = False,
|
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device_map = device_map,
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token = hf_token if hf_token and hf_token.strip() else None,
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trust_remote_code = trust_remote_code,
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)
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else:
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# Base model: download full HF repo, load from /LLM subfolder
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from huggingface_hub import snapshot_download
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|
|
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hf_repo = config.path
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local_dir = hf_repo.split("/")[-1]
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repo_path = snapshot_download(hf_repo, local_dir = local_dir)
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abs_repo_path = os.path.abspath(repo_path)
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llm_path = os.path.join(abs_repo_path, "LLM")
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logger.info(
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f"Spark-TTS: downloaded repo to {repo_path}, loading LLM from {llm_path}"
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)
|
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|
|
model, tokenizer = FastModel.from_pretrained(
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llm_path,
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dtype = torch.float32,
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load_in_4bit = False,
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device_map = device_map,
|
|
token = hf_token if hf_token and hf_token.strip() else None,
|
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trust_remote_code = trust_remote_code,
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)
|
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|
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FastModel.for_inference(model)
|
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self.models[model_name]["model"] = model
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|
self.models[model_name]["tokenizer"] = tokenizer
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self.models[model_name]["model_repo_path"] = abs_repo_path
|
|
elif audio_type == "dac":
|
|
# OuteTTS uses FastModel (not FastLanguageModel)
|
|
from unsloth import FastModel
|
|
|
|
model, tokenizer = FastModel.from_pretrained(
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config.path,
|
|
max_seq_length = max_seq_length,
|
|
load_in_4bit = False,
|
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device_map = device_map,
|
|
token = hf_token if hf_token and hf_token.strip() else None,
|
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trust_remote_code = trust_remote_code,
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)
|
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FastModel.for_inference(model)
|
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self.models[model_name]["model"] = model
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self.models[model_name]["tokenizer"] = tokenizer
|
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elif audio_type == "whisper":
|
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# Whisper ASR — uses FastModel with WhisperForConditionalGeneration
|
|
from unsloth import FastModel
|
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from transformers import WhisperForConditionalGeneration
|
|
|
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model, tokenizer = FastModel.from_pretrained(
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config.path,
|
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auto_model = WhisperForConditionalGeneration,
|
|
whisper_language = "English",
|
|
whisper_task = "transcribe",
|
|
load_in_4bit = False,
|
|
device_map = device_map,
|
|
token = hf_token if hf_token and hf_token.strip() else None,
|
|
trust_remote_code = trust_remote_code,
|
|
)
|
|
FastModel.for_inference(model)
|
|
model.eval()
|
|
|
|
# ASR pipeline (per notebook)
|
|
from transformers import pipeline as hf_pipeline
|
|
|
|
whisper_pipe = hf_pipeline(
|
|
"automatic-speech-recognition",
|
|
model = model,
|
|
tokenizer = tokenizer.tokenizer,
|
|
feature_extractor = tokenizer.feature_extractor,
|
|
processor = tokenizer,
|
|
return_language = True,
|
|
**dtype_kwargs(torch.float16),
|
|
)
|
|
self.models[model_name]["model"] = model
|
|
self.models[model_name]["tokenizer"] = tokenizer
|
|
self.models[model_name]["whisper_pipeline"] = whisper_pipe
|
|
else:
|
|
# SNAC (Orpheus) uses FastLanguageModel
|
|
model, tokenizer = FastLanguageModel.from_pretrained(
|
|
model_name = config.path,
|
|
max_seq_length = max_seq_length,
|
|
load_in_4bit = False,
|
|
device_map = device_map,
|
|
token = hf_token if hf_token and hf_token.strip() else None,
|
|
trust_remote_code = trust_remote_code,
|
|
)
|
|
FastLanguageModel.for_inference(model)
|
|
self.models[model_name]["model"] = model
|
|
self.models[model_name]["tokenizer"] = tokenizer
|
|
|
|
# Load external codec for TTS audio types
|
|
# (Whisper is ASR, audio_vlm is audio input — neither needs one)
|
|
if audio_type not in ("whisper", "audio_vlm"):
|
|
model_repo_path = self.models[model_name].get("model_repo_path")
|
|
self._audio_codec_manager.load_codec(
|
|
audio_type, self.device, model_repo_path = model_repo_path
|
|
)
|
|
|
|
# Reject CPU/disk offload for audio models too
|
|
raise_if_offloaded(self.models[model_name]["model"], device_map, "Inference")
|
|
self.models[model_name]["context_length"] = runtime_context_length(
|
|
self.models[model_name].get("model"),
|
|
max_seq_length,
|
|
)
|
|
|
|
self.active_model_name = model_name
|
|
self.loading_models.discard(model_name)
|
|
logger.info(f"Successfully loaded audio model: {model_name}")
|
|
log_gpu_memory(f"After loading {model_name}")
|
|
return True
|
|
|
|
model_type = "vision" if config.is_vision else "text"
|
|
adapter_info = " (LoRA adapter)" if self.models[model_name]["is_lora"] else ""
|
|
logger.info(f"Loading {model_type} model{adapter_info}: {model_name}")
|
|
log_gpu_memory(f"Before loading {model_name}")
|
|
|
|
# Same load path for base models and LoRA adapters
|
|
if config.is_vision:
|
|
# Vision model (or vision LoRA adapter)
|
|
model, processor = FastVisionModel.from_pretrained(
|
|
model_name = config.path, # Can be base model OR LoRA adapter path
|
|
max_seq_length = max_seq_length,
|
|
dtype = dtype,
|
|
load_in_4bit = load_in_4bit,
|
|
device_map = device_map,
|
|
token = hf_token if hf_token and hf_token.strip() else None,
|
|
trust_remote_code = trust_remote_code,
|
|
)
|
|
|
|
FastVisionModel.for_inference(model)
|
|
|
|
# FastVisionModel may return a raw tokenizer instead of a
|
|
# Processor for some models (e.g. Gemma-3); load the real one.
|
|
from transformers import ProcessorMixin
|
|
|
|
if not (
|
|
isinstance(processor, ProcessorMixin) or hasattr(processor, "image_processor")
|
|
):
|
|
# LoRA adapters: use base model. Local merged exports: read base from export_metadata.json.
|
|
# Non-LoRA: config.path, not config.identifier. They are the
|
|
# same for ordinary loads, but a local-only load rewrites
|
|
# path to the cached snapshot and this fallback must stay
|
|
# on those local files instead of refetching by repo id.
|
|
processor_source = config.base_model if config.is_lora else config.path
|
|
if not config.is_lora and config.is_local:
|
|
_meta_path = Path(config.path) / "export_metadata.json"
|
|
try:
|
|
if _meta_path.exists():
|
|
_meta = json.loads(_meta_path.read_text())
|
|
if _meta.get("base_model"):
|
|
processor_source = _meta["base_model"]
|
|
except Exception:
|
|
pass
|
|
logger.warning(
|
|
f"FastVisionModel returned {type(processor).__name__} (no image_processor) "
|
|
f"for '{model_name}' — loading proper processor from '{processor_source}'"
|
|
)
|
|
from transformers import AutoProcessor
|
|
|
|
# Local-only loads: a LoRA base or export_metadata base_model
|
|
# is a Hub repo id; resolve it from cache or fail the load
|
|
# (candidate failover) instead of downloading the processor.
|
|
processor = AutoProcessor.from_pretrained(
|
|
processor_source,
|
|
token = hf_token if hf_token and hf_token.strip() else None,
|
|
trust_remote_code = trust_remote_code,
|
|
local_files_only = local_files_only,
|
|
)
|
|
logger.info(f"Loaded {type(processor).__name__} from {processor_source}")
|
|
|
|
self.models[model_name]["model"] = model
|
|
self.models[model_name]["tokenizer"] = processor
|
|
self.models[model_name]["processor"] = processor
|
|
|
|
else:
|
|
# Text model (or text LoRA adapter)
|
|
model, tokenizer = FastLanguageModel.from_pretrained(
|
|
model_name = config.path, # Can be base model OR LoRA adapter path
|
|
max_seq_length = max_seq_length,
|
|
dtype = dtype,
|
|
load_in_4bit = load_in_4bit,
|
|
device_map = device_map,
|
|
token = hf_token if hf_token and hf_token.strip() else None,
|
|
trust_remote_code = trust_remote_code,
|
|
)
|
|
|
|
FastLanguageModel.for_inference(model)
|
|
|
|
self.models[model_name]["model"] = model
|
|
self.models[model_name]["tokenizer"] = tokenizer
|
|
|
|
raise_if_offloaded(self.models[model_name]["model"], device_map, "Inference")
|
|
self.models[model_name]["context_length"] = runtime_context_length(
|
|
self.models[model_name].get("model"),
|
|
max_seq_length,
|
|
)
|
|
|
|
self._resolve_chat_eos(model_name)
|
|
self._load_chat_template_info(model_name)
|
|
|
|
self.active_model_name = model_name
|
|
self.loading_models.discard(model_name)
|
|
|
|
logger.info(f"Successfully loaded model: {model_name}")
|
|
log_gpu_memory(f"After loading {model_name}")
|
|
return True
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to load model: {e}")
|
|
error_msg = format_error_message(e, config.identifier)
|
|
|
|
# Cleanup on failure
|
|
if model_name in self.models:
|
|
del self.models[model_name]
|
|
self.loading_models.discard(model_name)
|
|
|
|
raise Exception(error_msg)
|
|
|
|
def unload_model(self, model_name: str) -> bool:
|
|
"""Remove a model from the registry and clear GPU memory."""
|
|
if model_name in self.models:
|
|
try:
|
|
# Clean up codecs for audio models
|
|
if self.models[model_name].get("is_audio"):
|
|
self._audio_codec_manager.unload()
|
|
|
|
logger.info(f"Unloading model '{model_name}' from memory.")
|
|
del self.models[model_name]
|
|
|
|
# Clear the active model if it was the one unloaded
|
|
if self.active_model_name == model_name:
|
|
self.active_model_name = None
|
|
|
|
clear_gpu_cache()
|
|
|
|
# Drop stale compiled cache for the next model. On spawn platforms,
|
|
# preserve trainer files so concurrent dataset.map() workers can import them.
|
|
import sys as _sys
|
|
from utils.cache_cleanup import clear_unsloth_compiled_cache
|
|
|
|
_preserve = ["Unsloth*Trainer.py"] if _sys.platform in ("win32", "darwin") else None
|
|
clear_unsloth_compiled_cache(preserve_patterns = _preserve)
|
|
|
|
logger.info(f"Model '{model_name}' successfully unloaded.")
|
|
return True
|
|
except Exception as e:
|
|
logger.error(f"Error while unloading model '{model_name}': {e}")
|
|
return False
|
|
else:
|
|
logger.warning(
|
|
f"Attempted to unload model '{model_name}', but it was not found in the registry."
|
|
)
|
|
return True
|
|
|
|
def revert_to_base_model(self, base_model_name: str) -> bool:
|
|
"""Revert the model to its pristine base state by unloading and
|
|
deleting all adapter configurations."""
|
|
if base_model_name not in self.models:
|
|
return False
|
|
|
|
model = self.models[base_model_name].get("model")
|
|
|
|
try:
|
|
# Unload adapter weights if model is a PeftModel.
|
|
if isinstance(model, (PeftModel, PeftModelForCausalLM)):
|
|
logger.info(f"Unloading LoRA adapters from '{base_model_name}'...")
|
|
unwrapped_base_model = model.unload()
|
|
self.models[base_model_name]["model"] = unwrapped_base_model
|
|
model = unwrapped_base_model
|
|
|
|
# model.unload() can leave a peft_config; removing it avoids
|
|
# "multiple adapters" warnings on the next from_pretrained().
|
|
if hasattr(model, "peft_config"):
|
|
del model.peft_config
|
|
|
|
logger.info(f"Model '{base_model_name}' reverted to clean base state.")
|
|
return True
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to revert model to base state: {e}")
|
|
import traceback
|
|
|
|
logger.error(traceback.format_exc())
|
|
return False
|
|
|
|
def load_for_eval(
|
|
self,
|
|
lora_path: str,
|
|
max_seq_length: int = 2048,
|
|
dtype = None,
|
|
load_in_4bit: bool = True,
|
|
hf_token: Optional[str] = None,
|
|
gpu_ids: Optional[list[int]] = None,
|
|
) -> Tuple[bool, Optional[str], Optional[str]]:
|
|
"""Ensure the base model and the given adapter are loaded.
|
|
Idempotent and handles all states correctly.
|
|
"""
|
|
try:
|
|
from utils.models import ModelConfig
|
|
|
|
lora_config = ModelConfig.from_lora_path(lora_path, hf_token)
|
|
if not lora_config:
|
|
return False, None, None
|
|
|
|
base_model_name = lora_config.base_model
|
|
|
|
# 1. Load the base model if not already in memory
|
|
if base_model_name not in self.models or not self.models[base_model_name].get("model"):
|
|
logger.info(f"Base model '{base_model_name}' not loaded, loading now.")
|
|
base_config = ModelConfig.from_ui_selection(base_model_name, None, is_lora = False)
|
|
if not self.load_model(
|
|
base_config,
|
|
max_seq_length,
|
|
dtype,
|
|
load_in_4bit,
|
|
hf_token,
|
|
gpu_ids = gpu_ids,
|
|
):
|
|
return False, None, None
|
|
|
|
self.active_model_name = base_model_name
|
|
|
|
# 2. Derive adapter name from the user's selection
|
|
adapter_name = lora_path.split("/")[-1].replace(".", "_")
|
|
|
|
# 3. Ensure this adapter is loaded (load_adapter only reads from
|
|
# disk if the model doesn't already have it).
|
|
adapter_success = self.load_adapter(
|
|
base_model_name = base_model_name,
|
|
adapter_path = lora_path,
|
|
adapter_name = adapter_name,
|
|
)
|
|
if not adapter_success:
|
|
return False, base_model_name, None
|
|
|
|
# 4. Return the verified adapter name for the UI.
|
|
return True, base_model_name, adapter_name
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error during load_for_eval: {e}")
|
|
import traceback
|
|
|
|
logger.error(traceback.format_exc())
|
|
return False, None, None
|
|
|
|
def load_adapter(self, base_model_name: str, adapter_path: str, adapter_name: str) -> bool:
|
|
"""Load an adapter onto the model only if not already attached."""
|
|
model = self.models[base_model_name].get("model")
|
|
|
|
# Most reliable check: adapter name already in the model's config.
|
|
if hasattr(model, "peft_config") and adapter_name in model.peft_config:
|
|
logger.info(
|
|
f"Adapter '{adapter_name}' is already attached to the model. Skipping load."
|
|
)
|
|
return True
|
|
|
|
try:
|
|
logger.info(
|
|
f"Loading new adapter '{adapter_name}' from '{adapter_path}' onto {base_model_name}"
|
|
)
|
|
model.load_adapter(adapter_path, adapter_name = adapter_name)
|
|
|
|
# Update the registry only after a successful load.
|
|
if "loaded_adapters" not in self.models[base_model_name]:
|
|
self.models[base_model_name]["loaded_adapters"] = {}
|
|
self.models[base_model_name]["loaded_adapters"][adapter_name] = adapter_path
|
|
|
|
total_adapters = len(getattr(model, "peft_config", {}))
|
|
logger.info(
|
|
f"Adapter '{adapter_name}' loaded successfully. (Total unique adapters on model: {total_adapters})"
|
|
)
|
|
return True
|
|
except Exception as e:
|
|
logger.error(f"Failed to load adapter '{adapter_name}': {e}")
|
|
return False
|
|
|
|
def set_active_adapter(self, base_model_name: str, adapter_name: str) -> bool:
|
|
"""Set the active adapter for generation."""
|
|
model = self.models[base_model_name].get("model")
|
|
try:
|
|
logger.info(f"Setting active adapter to: '{adapter_name}'")
|
|
model.set_adapter(adapter_name)
|
|
self.models[base_model_name]["active_adapter"] = adapter_name
|
|
return True
|
|
except Exception as e:
|
|
# Catches "adapter not found" if something goes wrong.
|
|
logger.error(f"Failed to set active adapter to '{adapter_name}': {e}")
|
|
return False
|
|
|
|
def _apply_adapter_state(self, use_adapter: Optional[Union[bool, str]]) -> None:
|
|
"""Apply adapter state before generation (must hold _generation_lock).
|
|
|
|
Toggles PEFT enable/disable_adapter_layers (non-destructive, no reload).
|
|
use_adapter: None = no change, False = base model, True = current adapter,
|
|
str = named adapter.
|
|
"""
|
|
if use_adapter is None:
|
|
return
|
|
|
|
base = self.active_model_name
|
|
if not base or base not in self.models:
|
|
return
|
|
|
|
model_info = self.models[base]
|
|
model = model_info.get("model")
|
|
if model is None:
|
|
return
|
|
|
|
if use_adapter is False:
|
|
# Disable LoRA layers -> base model output.
|
|
if isinstance(model, (PeftModel, PeftModelForCausalLM)):
|
|
logger.info(
|
|
f"Compare mode: disabling adapters on '{base}' for base model generation"
|
|
)
|
|
model.base_model.disable_adapter_layers()
|
|
else:
|
|
logger.info(f"Compare mode: model '{base}' is not a PeftModel, already base")
|
|
|
|
elif use_adapter is True:
|
|
# Re-enable LoRA layers -> adapter output.
|
|
if isinstance(model, (PeftModel, PeftModelForCausalLM)):
|
|
logger.info(f"Compare mode: enabling adapters on '{base}' for LoRA generation")
|
|
model.base_model.enable_adapter_layers()
|
|
else:
|
|
logger.warning("use_adapter=true but model is not a PeftModel")
|
|
|
|
elif isinstance(use_adapter, str):
|
|
# Enable adapters and set the named one active.
|
|
if isinstance(model, (PeftModel, PeftModelForCausalLM)):
|
|
logger.info(f"Compare mode: enabling adapter '{use_adapter}' on '{base}'")
|
|
model.base_model.enable_adapter_layers()
|
|
self.set_active_adapter(base, use_adapter)
|
|
else:
|
|
logger.warning(f"use_adapter='{use_adapter}' but model is not a PeftModel")
|
|
|
|
def generate_with_adapter_control(
|
|
self,
|
|
use_adapter: Optional[Union[bool, str]] = None,
|
|
cancel_event = None,
|
|
**gen_kwargs,
|
|
) -> Generator[str, None, None]:
|
|
"""Thread-safe generation with optional adapter toggling.
|
|
|
|
Adapter toggle + model.generate() are serialized by _generation_lock in
|
|
the background thread, avoiding the RLock-reentrant race when two async
|
|
SSE handlers share one event-loop thread. use_adapter: see _apply_adapter_state.
|
|
"""
|
|
yield from self._generate_chat_response_inner(
|
|
cancel_event = cancel_event, _adapter_state = use_adapter, **gen_kwargs
|
|
)
|
|
|
|
def generate_chat_completion_with_tools(
|
|
self,
|
|
messages: list,
|
|
tools: list,
|
|
system_prompt: str = "",
|
|
temperature: float = 0.7,
|
|
top_p: float = 0.9,
|
|
top_k: int = 40,
|
|
min_p: float = 0.0,
|
|
max_new_tokens: int = 2048,
|
|
repetition_penalty: float = 1.0,
|
|
cancel_event = None,
|
|
enable_thinking: Optional[bool] = None,
|
|
reasoning_effort: Optional[str] = None,
|
|
preserve_thinking: Optional[bool] = None,
|
|
max_tool_iterations: int = 25,
|
|
auto_heal_tool_calls: bool = True,
|
|
nudge_tool_calls: Optional[bool] = None,
|
|
tool_call_timeout: int = 300,
|
|
session_id: Optional[str] = None,
|
|
thread_id: Optional[str] = None,
|
|
rag_scope: Optional[dict] = None,
|
|
presence_penalty: float = 0.0,
|
|
reasoning_prefilled: bool = False,
|
|
):
|
|
"""Run an agentic tool loop on top of ``generate_chat_response``.
|
|
|
|
Yields the same event-dict protocol as the GGUF path so the route
|
|
layer can stream both backends through one helper. Each event is one of:
|
|
|
|
* ``{"type": "status", "text": ...}``
|
|
* ``{"type": "content", "text": cumulative_text}``
|
|
* ``{"type": "tool_start", "tool_name", "tool_call_id", "arguments"}``
|
|
* ``{"type": "tool_end", "tool_name", "tool_call_id", "result"}``
|
|
"""
|
|
from core.inference.safetensors_agentic import run_safetensors_tool_loop
|
|
from core.inference.tools import execute_tool
|
|
|
|
def _single_turn(conv: list, *, active_tools: Optional[list[dict]] = None):
|
|
# conv already has the system message -- avoid double-prepend.
|
|
# `active_tools` is supplied by run_safetensors_tool_loop so one-shot
|
|
# tools such as render_html can be removed from later same-response prompts.
|
|
turn_tools = active_tools if active_tools is not None else tools
|
|
yield from self._generate_chat_response_inner(
|
|
messages = conv,
|
|
system_prompt = "",
|
|
temperature = temperature,
|
|
top_p = top_p,
|
|
top_k = top_k,
|
|
min_p = min_p,
|
|
max_new_tokens = max_new_tokens,
|
|
repetition_penalty = repetition_penalty,
|
|
cancel_event = cancel_event,
|
|
tools = turn_tools,
|
|
enable_thinking = enable_thinking,
|
|
reasoning_effort = reasoning_effort,
|
|
preserve_thinking = preserve_thinking,
|
|
presence_penalty = presence_penalty,
|
|
)
|
|
|
|
initial = list(messages)
|
|
if system_prompt:
|
|
initial = [{"role": "system", "content": system_prompt}] + initial
|
|
|
|
yield from run_safetensors_tool_loop(
|
|
single_turn = _single_turn,
|
|
messages = initial,
|
|
tools = tools,
|
|
execute_tool = execute_tool,
|
|
cancel_event = cancel_event,
|
|
auto_heal_tool_calls = auto_heal_tool_calls,
|
|
nudge_tool_calls = nudge_tool_calls,
|
|
max_tool_iterations = max_tool_iterations,
|
|
tool_call_timeout = tool_call_timeout,
|
|
session_id = session_id,
|
|
thread_id = thread_id,
|
|
rag_scope = rag_scope,
|
|
reasoning_prefilled = reasoning_prefilled,
|
|
)
|
|
|
|
def generate_chat_response(
|
|
self,
|
|
messages: list,
|
|
system_prompt: str,
|
|
image = None,
|
|
temperature: float = 0.7,
|
|
top_p: float = 0.9,
|
|
top_k: int = 40,
|
|
min_p: float = 0.0,
|
|
max_new_tokens: int = 256,
|
|
repetition_penalty: float = 1.0,
|
|
cancel_event = None,
|
|
tools: Optional[list] = None,
|
|
enable_thinking: Optional[bool] = None,
|
|
reasoning_effort: Optional[str] = None,
|
|
preserve_thinking: Optional[bool] = None,
|
|
presence_penalty: float = 0.0,
|
|
) -> Generator[str, None, None]:
|
|
"""Generate response for text or vision models (lock held by background thread).
|
|
|
|
``tools`` / ``enable_thinking`` / ``reasoning_effort`` / ``preserve_thinking``
|
|
are forwarded into ``apply_chat_template`` so templates that understand them
|
|
(Qwen3, Llama 3.1+, gpt-oss harmony) advertise tool schemas / reasoning controls.
|
|
``presence_penalty`` matches the GGUF sampling path (0 disables it).
|
|
"""
|
|
yield from self._generate_chat_response_inner(
|
|
messages = messages,
|
|
system_prompt = system_prompt,
|
|
image = image,
|
|
temperature = temperature,
|
|
top_p = top_p,
|
|
top_k = top_k,
|
|
min_p = min_p,
|
|
max_new_tokens = max_new_tokens,
|
|
repetition_penalty = repetition_penalty,
|
|
cancel_event = cancel_event,
|
|
tools = tools,
|
|
enable_thinking = enable_thinking,
|
|
reasoning_effort = reasoning_effort,
|
|
preserve_thinking = preserve_thinking,
|
|
presence_penalty = presence_penalty,
|
|
)
|
|
|
|
def _generate_chat_response_inner(
|
|
self,
|
|
messages: list,
|
|
system_prompt: str = "",
|
|
image = None,
|
|
temperature: float = 0.7,
|
|
top_p: float = 0.9,
|
|
top_k: int = 40,
|
|
min_p: float = 0.0,
|
|
max_new_tokens: int = 256,
|
|
repetition_penalty: float = 1.0,
|
|
cancel_event = None,
|
|
_adapter_state = None,
|
|
tools: Optional[list] = None,
|
|
enable_thinking: Optional[bool] = None,
|
|
reasoning_effort: Optional[str] = None,
|
|
preserve_thinking: Optional[bool] = None,
|
|
presence_penalty: float = 0.0,
|
|
) -> Generator[str, None, None]:
|
|
"""Inner generation logic, called by generate_chat_response and
|
|
generate_with_adapter_control.
|
|
|
|
_adapter_state is passed to generate_stream/vision so the background
|
|
thread can toggle adapters under the generation lock.
|
|
"""
|
|
if not self.active_model_name:
|
|
raise RuntimeError("No active model")
|
|
|
|
model_info = self.models[self.active_model_name]
|
|
is_vision = model_info.get("is_vision", False)
|
|
tokenizer = model_info.get("tokenizer") or model_info.get("processor")
|
|
# Unwrap processor -> raw tokenizer for VLMs on the text path.
|
|
tokenizer = getattr(tokenizer, "tokenizer", tokenizer)
|
|
top_k = self._normalize_top_k(top_k)
|
|
|
|
if is_vision and image:
|
|
# Verify the stored processor can handle images; FastVisionModel may
|
|
# return a raw tokenizer instead of a ProcessorMixin (e.g. Gemma-3).
|
|
from transformers import ProcessorMixin
|
|
|
|
processor = model_info.get("processor")
|
|
has_image_processing = processor is not None and (
|
|
isinstance(processor, ProcessorMixin) or hasattr(processor, "image_processor")
|
|
)
|
|
if has_image_processing:
|
|
yield from self._generate_vision_response(
|
|
messages,
|
|
system_prompt,
|
|
image,
|
|
temperature,
|
|
top_p,
|
|
top_k,
|
|
min_p,
|
|
max_new_tokens,
|
|
repetition_penalty,
|
|
cancel_event = cancel_event,
|
|
presence_penalty = presence_penalty,
|
|
)
|
|
return
|
|
else:
|
|
logger.warning(
|
|
f"Model '{self.active_model_name}' is marked as vision but its processor "
|
|
f"({type(processor).__name__}) has no image_processor — "
|
|
f"falling back to text-only generation (image will be ignored)."
|
|
)
|
|
|
|
# Text path: messages are already in ChatML format from eval.py.
|
|
|
|
# Step 1: apply get_chat_template if model is in mapper.
|
|
try:
|
|
from utils.datasets import (
|
|
MODEL_TO_TEMPLATE_MAPPER,
|
|
get_tokenizer_chat_template,
|
|
)
|
|
model_name_lower = self.active_model_name.lower()
|
|
|
|
if model_name_lower in MODEL_TO_TEMPLATE_MAPPER:
|
|
template_name = MODEL_TO_TEMPLATE_MAPPER[model_name_lower]
|
|
logger.info(
|
|
f"Applying chat template '{template_name}' for {self.active_model_name}"
|
|
)
|
|
|
|
tokenizer = get_chat_template(
|
|
tokenizer,
|
|
chat_template = template_name,
|
|
)
|
|
# The mapper installs the effective template only now, at generate
|
|
# time, so re-resolve and UNION into the load-time cache (never
|
|
# overwrite). get_chat_template can return a remapped tokenizer
|
|
# (turn-end folded onto doc-eos) while generate_stream reads the
|
|
# original, so take marker strings from the mapped template but
|
|
# resolve their ids on the original.
|
|
try:
|
|
_gen_tok = model_info.get("tokenizer") or tokenizer
|
|
refreshed = resolve_chat_turn_end_eos_ids_using(
|
|
getattr(tokenizer, "tokenizer", tokenizer),
|
|
getattr(_gen_tok, "tokenizer", _gen_tok),
|
|
)
|
|
existing = model_info.get("chat_turn_end_eos_ids") or []
|
|
model_info["chat_turn_end_eos_ids"] = sorted(set(existing) | set(refreshed))
|
|
except Exception as e:
|
|
logger.warning(f"Could not refresh chat turn-end eos after template: {e}")
|
|
else:
|
|
logger.info(
|
|
f"No registered Unsloth template for {self.active_model_name}, using tokenizer default"
|
|
)
|
|
except Exception as e:
|
|
logger.warning(f"Could not apply get_chat_template: {e}")
|
|
|
|
# Step 2: format with tokenizer.apply_chat_template().
|
|
if system_prompt:
|
|
template_messages = [{"role": "system", "content": system_prompt}] + messages
|
|
else:
|
|
template_messages = messages
|
|
reasoning_channel_markers_resolved = False
|
|
try:
|
|
if not (hasattr(tokenizer, "chat_template") and tokenizer.chat_template):
|
|
raise ValueError(
|
|
f"Model '{self.active_model_name}' has no chat_template set in its "
|
|
f"tokenizer_config.json. This is usually a problem with the model's "
|
|
f"HuggingFace repository — it is missing a 'chat_template' key. "
|
|
f"Please use a model that includes a chat template, or manually set "
|
|
f"one via tokenizer.chat_template before inference."
|
|
)
|
|
reasoning_channel_markers = None
|
|
formatted_prompt = self._apply_chat_template_for_generation(
|
|
tokenizer,
|
|
template_messages,
|
|
tools = tools,
|
|
enable_thinking = enable_thinking,
|
|
reasoning_effort = reasoning_effort,
|
|
preserve_thinking = preserve_thinking,
|
|
)
|
|
|
|
# If tools were requested but the (possibly overridden) template ignored
|
|
# them, fall back to the model's native template (shared with MLX).
|
|
from core.inference.chat_template_helpers import (
|
|
render_with_native_template_fallback,
|
|
)
|
|
|
|
render_result = render_with_native_template_fallback(
|
|
formatted_prompt = formatted_prompt,
|
|
tokenizer = tokenizer,
|
|
model_info = model_info,
|
|
active_model_name = self.active_model_name,
|
|
messages = template_messages,
|
|
tools = tools,
|
|
enable_thinking = enable_thinking,
|
|
reasoning_effort = reasoning_effort,
|
|
preserve_thinking = preserve_thinking,
|
|
apply_fn = self._apply_chat_template_for_generation,
|
|
hf_token = model_info.get("hf_token"),
|
|
return_metadata = True,
|
|
)
|
|
formatted_prompt = render_result.prompt
|
|
reasoning_channel_markers = render_result.reasoning_channel_markers
|
|
reasoning_channel_markers_resolved = True
|
|
|
|
logger.debug(f"Formatted prompt: {formatted_prompt[:200]}...")
|
|
except Exception as e:
|
|
logger.error(f"Error applying chat template: {e}")
|
|
# Fall back to manual formatting
|
|
formatted_prompt = self.format_chat_prompt(messages, system_prompt)
|
|
reasoning_channel_markers = None
|
|
reasoning_channel_markers_resolved = True
|
|
|
|
# Step 3: generate
|
|
yield from self.generate_stream(
|
|
formatted_prompt,
|
|
temperature,
|
|
top_p,
|
|
top_k,
|
|
min_p,
|
|
max_new_tokens,
|
|
repetition_penalty,
|
|
cancel_event = cancel_event,
|
|
_adapter_state = _adapter_state,
|
|
presence_penalty = presence_penalty,
|
|
reasoning_channel_markers = reasoning_channel_markers,
|
|
reasoning_channel_markers_resolved = reasoning_channel_markers_resolved,
|
|
)
|
|
|
|
def _generate_vision_response(
|
|
self,
|
|
messages,
|
|
system_prompt,
|
|
image,
|
|
temperature,
|
|
top_p,
|
|
top_k,
|
|
min_p,
|
|
max_new_tokens,
|
|
repetition_penalty,
|
|
cancel_event = None,
|
|
presence_penalty: float = 0.0,
|
|
) -> Generator[str, None, None]:
|
|
"""Handle vision model generation with true token-by-token streaming."""
|
|
model_info = self.models[self.active_model_name]
|
|
model = model_info["model"]
|
|
processor = model_info["processor"]
|
|
# FastVisionModel may return a raw tokenizer (e.g. GemmaTokenizerFast)
|
|
# for some models. Safe unwrap for tokenize-only ops.
|
|
raw_tokenizer = getattr(processor, "tokenizer", processor)
|
|
|
|
# Extract user message
|
|
user_message = ""
|
|
if messages and messages[-1]["role"] == "user":
|
|
import re
|
|
user_message = content_to_text(messages[-1]["content"])
|
|
user_message = re.sub(r"<img[^>]*>", "", user_message).strip()
|
|
|
|
if not user_message:
|
|
user_message = "Describe this image." if image else "Hello"
|
|
|
|
# Prepare vision messages
|
|
if image:
|
|
user_msg = {
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "image"},
|
|
{"type": "text", "text": user_message},
|
|
],
|
|
}
|
|
if system_prompt:
|
|
vision_messages = [
|
|
{
|
|
"role": "system",
|
|
"content": [{"type": "text", "text": system_prompt}],
|
|
},
|
|
user_msg,
|
|
]
|
|
else:
|
|
vision_messages = [user_msg]
|
|
|
|
try:
|
|
input_text = processor.apply_chat_template(
|
|
vision_messages, add_generation_prompt = True, tokenize = False
|
|
)
|
|
except Exception as e:
|
|
if system_prompt:
|
|
logger.warning(
|
|
f"Vision processor for '{self.active_model_name}' may not support "
|
|
f"system messages; retrying without. Original error: {e}"
|
|
)
|
|
vision_messages = [user_msg]
|
|
input_text = processor.apply_chat_template(
|
|
vision_messages, add_generation_prompt = True, tokenize = False
|
|
)
|
|
else:
|
|
raise
|
|
inputs = processor(
|
|
image,
|
|
input_text,
|
|
add_special_tokens = False,
|
|
return_tensors = "pt",
|
|
).to(model.device)
|
|
prompt_text = input_text
|
|
else:
|
|
# Text-only path for a vision model
|
|
formatted_prompt = self.format_chat_prompt(messages, system_prompt)
|
|
inputs = raw_tokenizer(formatted_prompt, return_tensors = "pt").to(model.device)
|
|
prompt_text = formatted_prompt
|
|
|
|
# Stream with TextIteratorStreamer + background thread
|
|
try:
|
|
# Re-emit an open <think> prefill swallowed by skip_prompt (see
|
|
# generate_stream).
|
|
think_prefix = detect_think_prefill(
|
|
prompt_text, getattr(raw_tokenizer, "all_special_tokens", None)
|
|
)
|
|
import threading
|
|
|
|
streamer = self._make_text_streamer(
|
|
raw_tokenizer,
|
|
protocol_source = processor,
|
|
# The text-only VLM fallback above did not render with the
|
|
# processor template, so its native markers do not describe
|
|
# this request's response protocol.
|
|
reasoning_channel_markers = detect_reasoning_channel_markers(processor)
|
|
if image
|
|
else None,
|
|
reasoning_channel_markers_resolved = True,
|
|
skip_prompt = True,
|
|
timeout = 0.2,
|
|
cancel_event = cancel_event,
|
|
use_harmony = self._is_gpt_oss_model(),
|
|
)
|
|
|
|
generation_kwargs = dict(
|
|
**inputs,
|
|
streamer = streamer,
|
|
max_new_tokens = max_new_tokens,
|
|
use_cache = True,
|
|
do_sample = temperature > 0,
|
|
temperature = temperature,
|
|
top_p = top_p,
|
|
top_k = top_k,
|
|
min_p = min_p,
|
|
)
|
|
# Presence penalty (GGUF parity) for VLM chat.
|
|
_vision_input_ids = inputs.get("input_ids") if hasattr(inputs, "get") else None
|
|
if _vision_input_ids is not None:
|
|
_pp = _make_presence_penalty_processor(
|
|
presence_penalty, int(_vision_input_ids.shape[1])
|
|
)
|
|
if _pp is not None:
|
|
generation_kwargs["logits_processor"] = _pp
|
|
stopping_criteria = self._cancel_stopping_criteria(cancel_event)
|
|
if stopping_criteria is not None:
|
|
generation_kwargs["stopping_criteria"] = stopping_criteria
|
|
active_stop_token_ids = self._generation_stop_token_ids(model, generation_kwargs)
|
|
|
|
err: dict[str, str] = {}
|
|
|
|
def generate_fn():
|
|
with self._generation_lock:
|
|
try:
|
|
model.generate(**generation_kwargs)
|
|
except Exception as e:
|
|
err["msg"] = str(e)
|
|
if hasattr(streamer, "abort"):
|
|
streamer.abort()
|
|
logger.error(f"Vision generation error in thread: {e}")
|
|
finally:
|
|
try:
|
|
streamer.end()
|
|
except Exception:
|
|
pass
|
|
|
|
thread = threading.Thread(target = generate_fn)
|
|
thread.start()
|
|
|
|
output = think_prefix
|
|
# Emit the prefilled <think> before the first token so the block
|
|
# renders during prompt prefill (which can take seconds).
|
|
if think_prefix:
|
|
yield think_prefix
|
|
from queue import Empty
|
|
import time
|
|
|
|
generation_complete = False
|
|
cancel_deadline = None
|
|
try:
|
|
while True:
|
|
if cancel_event is not None and cancel_event.is_set():
|
|
if cancel_deadline is None:
|
|
cancel_deadline = time.monotonic() + 10
|
|
elif time.monotonic() >= cancel_deadline:
|
|
break
|
|
try:
|
|
new_token = next(streamer)
|
|
except StopIteration:
|
|
generation_complete = True
|
|
break
|
|
except Empty:
|
|
if not thread.is_alive():
|
|
generation_complete = True
|
|
output = yield from self._drain_streamer_tail(
|
|
streamer, output, active_stop_token_ids
|
|
)
|
|
break
|
|
if cancel_deadline is not None:
|
|
remaining = cancel_deadline - time.monotonic()
|
|
if remaining <= 0:
|
|
break
|
|
thread.join(timeout = remaining)
|
|
if thread.is_alive():
|
|
break
|
|
generation_complete = True
|
|
output = yield from self._drain_streamer_tail(
|
|
streamer, output, active_stop_token_ids
|
|
)
|
|
break
|
|
continue
|
|
if new_token:
|
|
output, cleaned = self._append_stream_delta(
|
|
output, new_token, active_stop_token_ids
|
|
)
|
|
yield cleaned
|
|
finally:
|
|
if cancel_event is not None and not generation_complete:
|
|
cancel_event.set()
|
|
join_timeout = 10
|
|
if cancel_deadline is not None:
|
|
join_timeout = max(0, cancel_deadline - time.monotonic())
|
|
thread.join(timeout = join_timeout)
|
|
if thread.is_alive():
|
|
logger.warning(
|
|
"Vision generation thread did not exit after cancel/join timeout"
|
|
)
|
|
|
|
if err.get("msg"):
|
|
raise _GenerationThreadError(err["msg"])
|
|
|
|
except _GenerationThreadError:
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"Vision generation error: {e}")
|
|
raise
|
|
|
|
def generate_audio_input_response(
|
|
self,
|
|
messages,
|
|
system_prompt,
|
|
audio_array,
|
|
temperature,
|
|
top_p,
|
|
top_k,
|
|
min_p,
|
|
max_new_tokens,
|
|
repetition_penalty,
|
|
cancel_event = None,
|
|
) -> Generator[str, None, None]:
|
|
"""Audio-input (ASR) generation: takes an audio numpy array, streams text.
|
|
|
|
Uses processor.apply_chat_template with audio embedded in messages (Gemma 3n pattern).
|
|
"""
|
|
import threading
|
|
import numpy as np
|
|
|
|
model_info = self.models[self.active_model_name]
|
|
model = model_info["model"]
|
|
processor = model_info.get("processor") or model_info.get("tokenizer")
|
|
raw_tokenizer = getattr(processor, "tokenizer", processor)
|
|
|
|
# Last user text; default matches the notebook prompt
|
|
user_text = "Please transcribe this audio."
|
|
if messages:
|
|
for msg in reversed(messages):
|
|
if msg["role"] == "user" and msg.get("content"):
|
|
user_text = content_to_text(msg["content"])
|
|
break
|
|
|
|
# ASR-specific default system prompt if none set
|
|
if not system_prompt:
|
|
system_prompt = "You are an assistant that transcribes speech accurately."
|
|
|
|
# Gemma 3n format — audio goes INTO apply_chat_template
|
|
audio_messages = [
|
|
{"role": "system", "content": [{"type": "text", "text": system_prompt}]},
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "audio", "audio": audio_array},
|
|
{"type": "text", "text": user_text},
|
|
],
|
|
},
|
|
]
|
|
|
|
# apply_chat_template does audio embedding + tokenization in one step
|
|
inputs = processor.apply_chat_template(
|
|
audio_messages,
|
|
add_generation_prompt = True,
|
|
tokenize = True,
|
|
return_dict = True,
|
|
return_tensors = "pt",
|
|
truncation = False,
|
|
).to(model.device)
|
|
|
|
try:
|
|
from transformers import TextIteratorStreamer
|
|
from queue import Empty
|
|
|
|
streamer = TextIteratorStreamer(
|
|
raw_tokenizer,
|
|
skip_prompt = True,
|
|
skip_special_tokens = True,
|
|
timeout = 0.2,
|
|
)
|
|
|
|
# Notebook uses do_sample=False (greedy) for ASR accuracy
|
|
generation_kwargs = dict(
|
|
**inputs,
|
|
streamer = streamer,
|
|
max_new_tokens = max_new_tokens,
|
|
use_cache = True,
|
|
do_sample = False,
|
|
)
|
|
|
|
err: dict[str, str] = {}
|
|
|
|
def generate_fn():
|
|
with self._generation_lock:
|
|
try:
|
|
model.generate(**generation_kwargs)
|
|
except Exception as e:
|
|
err["msg"] = str(e)
|
|
logger.error(f"Audio input generation error in thread: {e}")
|
|
finally:
|
|
try:
|
|
streamer.end()
|
|
except Exception:
|
|
pass
|
|
|
|
thread = threading.Thread(target = generate_fn)
|
|
thread.start()
|
|
|
|
output = ""
|
|
try:
|
|
while True:
|
|
if cancel_event is not None and cancel_event.is_set():
|
|
break
|
|
try:
|
|
new_token = next(streamer)
|
|
except StopIteration:
|
|
break
|
|
except Empty:
|
|
if not thread.is_alive():
|
|
break
|
|
continue
|
|
if new_token:
|
|
output += new_token
|
|
yield new_token
|
|
finally:
|
|
if cancel_event is not None:
|
|
cancel_event.set()
|
|
thread.join(timeout = 10)
|
|
if thread.is_alive():
|
|
logger.warning(
|
|
"Audio input generation thread did not exit after cancel/join timeout"
|
|
)
|
|
|
|
if err.get("msg"):
|
|
raise _GenerationThreadError(err["msg"])
|
|
|
|
except _GenerationThreadError:
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"Audio input generation error: {e}")
|
|
raise
|
|
|
|
def generate_whisper_response(
|
|
self,
|
|
audio_array,
|
|
cancel_event = None,
|
|
) -> Generator[str, None, None]:
|
|
"""Whisper ASR: takes an audio numpy array, yields transcribed text.
|
|
|
|
Uses the pre-built transformers pipeline created at model load.
|
|
"""
|
|
model_info = self.models[self.active_model_name]
|
|
whisper_pipe = model_info.get("whisper_pipeline")
|
|
if not whisper_pipe:
|
|
yield "Error: Whisper pipeline not initialized"
|
|
return
|
|
|
|
try:
|
|
with self._generation_lock:
|
|
result = whisper_pipe({"raw": audio_array, "sampling_rate": 16000})
|
|
|
|
text = result.get("text", "") if isinstance(result, dict) else str(result)
|
|
if text:
|
|
yield text
|
|
except Exception as e:
|
|
logger.error(f"Whisper ASR error: {e}")
|
|
yield f"Error: {str(e)}"
|
|
|
|
def _is_gpt_oss_model(self, model_name: str = None) -> bool:
|
|
"""Whether the given (or active) model uses the gpt-oss harmony protocol."""
|
|
from utils.datasets import is_gpt_oss_model_name
|
|
return is_gpt_oss_model_name(model_name or self.active_model_name or "")
|
|
|
|
def _make_text_streamer(
|
|
self,
|
|
tokenizer,
|
|
*,
|
|
protocol_source = None,
|
|
reasoning_channel_markers = None,
|
|
reasoning_channel_markers_resolved: bool = False,
|
|
skip_prompt: bool = True,
|
|
timeout: float = 0.2,
|
|
cancel_event = None,
|
|
use_harmony: bool = False,
|
|
):
|
|
"""Create the streamer matching this model's native response protocol."""
|
|
if use_harmony:
|
|
try:
|
|
return HarmonyTextStreamer(
|
|
tokenizer,
|
|
skip_prompt = skip_prompt,
|
|
timeout = timeout,
|
|
)
|
|
except Exception as e:
|
|
logger.warning(f"HarmonyTextStreamer init failed, falling back: {e}")
|
|
return TextIteratorStreamer(
|
|
tokenizer,
|
|
skip_prompt = skip_prompt,
|
|
skip_special_tokens = True,
|
|
timeout = timeout,
|
|
)
|
|
|
|
markers = (
|
|
reasoning_channel_markers
|
|
if reasoning_channel_markers_resolved
|
|
else reasoning_channel_markers
|
|
or detect_reasoning_channel_markers(protocol_source or tokenizer)
|
|
)
|
|
if markers is not None:
|
|
return ReasoningTextIteratorStreamer(
|
|
tokenizer,
|
|
markers = markers,
|
|
skip_prompt = skip_prompt,
|
|
timeout = timeout,
|
|
cancel_event = cancel_event,
|
|
)
|
|
return TextIteratorStreamer(
|
|
tokenizer,
|
|
skip_prompt = skip_prompt,
|
|
skip_special_tokens = True,
|
|
timeout = timeout,
|
|
)
|
|
|
|
def _append_stream_delta(
|
|
self,
|
|
output: str,
|
|
new_token: str,
|
|
stop_token_ids = None,
|
|
):
|
|
"""Append a streamer delta and apply response-boundary cleanup."""
|
|
output += new_token
|
|
return output, self._clean_generated_text(output, stop_token_ids = stop_token_ids)
|
|
|
|
def _drain_streamer_tail(
|
|
self,
|
|
streamer,
|
|
output: str,
|
|
stop_token_ids = None,
|
|
):
|
|
"""Drain queued streamer text after the producer exits."""
|
|
while True:
|
|
try:
|
|
new_token = next(streamer)
|
|
except StopIteration:
|
|
return output
|
|
except Exception:
|
|
return output
|
|
if new_token:
|
|
output, cleaned = self._append_stream_delta(
|
|
output, new_token, stop_token_ids = stop_token_ids
|
|
)
|
|
yield cleaned
|
|
|
|
def generate_stream(
|
|
self,
|
|
prompt: str,
|
|
temperature: float = 0.7,
|
|
top_p: float = 0.9,
|
|
top_k: int = 40,
|
|
min_p: float = 0.0,
|
|
max_new_tokens: int = 256,
|
|
repetition_penalty: float = 1.0,
|
|
cancel_event = None,
|
|
_adapter_state = None,
|
|
presence_penalty: float = 0.0,
|
|
reasoning_channel_markers = None,
|
|
reasoning_channel_markers_resolved: bool = False,
|
|
) -> Generator[str, None, None]:
|
|
"""Generate a streaming text response (text models only).
|
|
|
|
_adapter_state: if not None, the background thread toggles adapters
|
|
before model.generate(), under _generation_lock.
|
|
``presence_penalty`` matches the GGUF sampling path via a logits processor (0 disables it).
|
|
"""
|
|
if not self.active_model_name:
|
|
raise RuntimeError("No active model")
|
|
|
|
model_info = self.models[self.active_model_name]
|
|
model = model_info["model"]
|
|
# For VLMs the stored "tokenizer" is actually the processor. Unwrap to
|
|
# the real tokenizer so TextIteratorStreamer's skip_prompt /
|
|
# skip_special_tokens work correctly.
|
|
tokenizer = model_info["tokenizer"]
|
|
tokenizer = getattr(tokenizer, "tokenizer", tokenizer)
|
|
|
|
try:
|
|
inputs = tokenizer(prompt, return_tensors = "pt").to(model.device)
|
|
|
|
import threading
|
|
|
|
# skip_prompt swallows an open <think> prefilled by the template;
|
|
# re-emit it so the frontend can render the thinking block.
|
|
# gpt-oss emits its own tags via HarmonyTextStreamer.
|
|
think_prefix = (
|
|
""
|
|
if self._is_gpt_oss_model()
|
|
else detect_think_prefill(prompt, getattr(tokenizer, "all_special_tokens", None))
|
|
)
|
|
|
|
streamer = self._make_text_streamer(
|
|
tokenizer,
|
|
protocol_source = model_info.get("tokenizer"),
|
|
reasoning_channel_markers = reasoning_channel_markers,
|
|
reasoning_channel_markers_resolved = reasoning_channel_markers_resolved,
|
|
skip_prompt = True,
|
|
timeout = 0.2,
|
|
cancel_event = cancel_event,
|
|
use_harmony = self._is_gpt_oss_model(),
|
|
)
|
|
|
|
generation_kwargs = dict(
|
|
**inputs,
|
|
streamer = streamer,
|
|
max_new_tokens = max_new_tokens,
|
|
temperature = temperature,
|
|
top_p = top_p,
|
|
top_k = top_k,
|
|
min_p = min_p,
|
|
repetition_penalty = repetition_penalty,
|
|
do_sample = temperature > 0,
|
|
# Resolved once at load (chat_template-derived turn-end tokens).
|
|
eos_token_id = model_info.get("chat_turn_end_eos_ids") or tokenizer.eos_token_id,
|
|
pad_token_id = tokenizer.eos_token_id
|
|
if tokenizer.pad_token_id is None
|
|
else tokenizer.pad_token_id,
|
|
)
|
|
active_stop_token_ids = self._generation_stop_token_ids(model, generation_kwargs)
|
|
# Presence penalty (GGUF parity); prompt_len excludes prompt tokens.
|
|
_pp = _make_presence_penalty_processor(
|
|
presence_penalty, int(inputs["input_ids"].shape[1])
|
|
)
|
|
if _pp is not None:
|
|
generation_kwargs["logits_processor"] = _pp
|
|
stopping_criteria = self._cancel_stopping_criteria(cancel_event)
|
|
if stopping_criteria is not None:
|
|
generation_kwargs["stopping_criteria"] = stopping_criteria
|
|
|
|
def generate_fn():
|
|
with self._generation_lock:
|
|
try:
|
|
if _adapter_state is not None:
|
|
self._apply_adapter_state(_adapter_state)
|
|
model.generate(**generation_kwargs)
|
|
except Exception as e:
|
|
err["msg"] = str(e)
|
|
if hasattr(streamer, "abort"):
|
|
streamer.abort()
|
|
logger.error(f"Generation error: {e}")
|
|
finally:
|
|
try:
|
|
streamer.end()
|
|
except Exception:
|
|
pass
|
|
|
|
err: dict[str, str] = {}
|
|
thread = threading.Thread(target = generate_fn)
|
|
thread.start()
|
|
|
|
output = think_prefix
|
|
# Emit the prefilled <think> before the first token so the block
|
|
# renders during prompt prefill (which can take seconds).
|
|
if think_prefix:
|
|
yield think_prefix
|
|
from queue import Empty
|
|
import time
|
|
|
|
generation_complete = False
|
|
cancel_deadline = None
|
|
try:
|
|
while True:
|
|
if cancel_event is not None and cancel_event.is_set():
|
|
if cancel_deadline is None:
|
|
cancel_deadline = time.monotonic() + 10
|
|
elif time.monotonic() >= cancel_deadline:
|
|
break
|
|
try:
|
|
new_token = next(streamer)
|
|
except StopIteration:
|
|
generation_complete = True
|
|
break
|
|
except Empty:
|
|
if not thread.is_alive():
|
|
generation_complete = True
|
|
output = yield from self._drain_streamer_tail(
|
|
streamer, output, active_stop_token_ids
|
|
)
|
|
break
|
|
if cancel_deadline is not None:
|
|
remaining = cancel_deadline - time.monotonic()
|
|
if remaining <= 0:
|
|
break
|
|
thread.join(timeout = remaining)
|
|
if thread.is_alive():
|
|
break
|
|
generation_complete = True
|
|
output = yield from self._drain_streamer_tail(
|
|
streamer, output, active_stop_token_ids
|
|
)
|
|
break
|
|
continue
|
|
if new_token:
|
|
output, cleaned = self._append_stream_delta(
|
|
output, new_token, active_stop_token_ids
|
|
)
|
|
yield cleaned
|
|
finally:
|
|
# Set cancel_event only on early exit (user cancel), NOT on
|
|
# normal completion. It's a shared mp.Event; setting it
|
|
# unconditionally would leave a stale cancel signal that could
|
|
# disrupt the next serialized request (e.g. compare mode).
|
|
if cancel_event is not None and not generation_complete:
|
|
cancel_event.set()
|
|
join_timeout = 10
|
|
if cancel_deadline is not None:
|
|
join_timeout = max(0, cancel_deadline - time.monotonic())
|
|
thread.join(timeout = join_timeout)
|
|
if thread.is_alive():
|
|
logger.warning("Generation thread did not exit after cancel/join timeout")
|
|
|
|
if err.get("msg"):
|
|
raise _GenerationThreadError(err["msg"])
|
|
|
|
except _GenerationThreadError:
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"Error during generation: {e}")
|
|
raise
|
|
|
|
# ── Audio (TTS) Generation ────────────────────────────────────
|
|
|
|
def generate_audio_response(
|
|
self,
|
|
text: str,
|
|
temperature: float = 0.6,
|
|
top_p: float = 0.95,
|
|
top_k: int = 50,
|
|
min_p: float = 0.0,
|
|
max_new_tokens: int = 2048,
|
|
repetition_penalty: float = 1.0,
|
|
use_adapter: Optional[Union[bool, str]] = None,
|
|
) -> Tuple[bytes, int]:
|
|
"""Generate audio from text for TTS models.
|
|
Returns (wav_bytes, sample_rate). Blocking — full audio before return.
|
|
"""
|
|
if not self.active_model_name:
|
|
raise RuntimeError("No active model")
|
|
|
|
model_info = self.models[self.active_model_name]
|
|
audio_type = model_info.get("audio_type")
|
|
model = model_info["model"]
|
|
tokenizer = model_info.get("tokenizer")
|
|
|
|
if not audio_type:
|
|
raise RuntimeError(f"Model {self.active_model_name} is not an audio model")
|
|
|
|
top_k = self._normalize_top_k(top_k)
|
|
|
|
with self._generation_lock:
|
|
if use_adapter is not None:
|
|
self._apply_adapter_state(use_adapter)
|
|
|
|
if audio_type == "snac":
|
|
return self._generate_snac(
|
|
model,
|
|
tokenizer,
|
|
text,
|
|
temperature,
|
|
top_p,
|
|
max_new_tokens,
|
|
repetition_penalty,
|
|
)
|
|
elif audio_type == "csm":
|
|
processor = model_info.get("processor", tokenizer)
|
|
return self._generate_csm(model, processor, text, max_new_tokens)
|
|
elif audio_type == "bicodec":
|
|
return self._generate_bicodec(
|
|
model, tokenizer, text, temperature, top_k, max_new_tokens
|
|
)
|
|
elif audio_type == "dac":
|
|
return self._generate_dac(
|
|
model,
|
|
tokenizer,
|
|
text,
|
|
temperature,
|
|
top_k,
|
|
top_p,
|
|
min_p,
|
|
max_new_tokens,
|
|
repetition_penalty,
|
|
)
|
|
else:
|
|
raise RuntimeError(f"Unknown audio_type: {audio_type}")
|
|
|
|
def _generate_snac(
|
|
self, model, tokenizer, text, temperature, top_p, max_new_tokens, repetition_penalty
|
|
):
|
|
"""Generate audio using SNAC codec (Orpheus)."""
|
|
device = model.device
|
|
start_token = torch.tensor([[128259]], device = device) # START_OF_HUMAN
|
|
end_tokens = torch.tensor([[128009, 128260]], device = device) # EOT, END_OF_HUMAN
|
|
text_ids = tokenizer(text, return_tensors = "pt").input_ids.to(device)
|
|
input_ids = torch.cat([start_token, text_ids, end_tokens], dim = 1)
|
|
attention_mask = torch.ones_like(input_ids)
|
|
|
|
generated = model.generate(
|
|
input_ids = input_ids,
|
|
attention_mask = attention_mask,
|
|
max_new_tokens = max_new_tokens,
|
|
do_sample = True,
|
|
temperature = temperature,
|
|
top_p = top_p,
|
|
repetition_penalty = repetition_penalty,
|
|
eos_token_id = 128258, # END_OF_SPEECH
|
|
use_cache = True,
|
|
)
|
|
return self._audio_codec_manager.decode_snac(generated, str(device))
|
|
|
|
def _generate_csm(self, model, processor, text, max_new_tokens):
|
|
"""Generate audio using CSM (Sesame)."""
|
|
speaker_id = 0
|
|
inputs = processor(
|
|
f"[{speaker_id}]{text}", add_special_tokens = True, return_tensors = "pt"
|
|
).to(model.device)
|
|
audio_values = model.generate(**inputs, max_new_tokens = max_new_tokens, output_audio = True)
|
|
return self._audio_codec_manager.decode_csm(audio_values)
|
|
|
|
def _generate_bicodec(self, model, tokenizer, text, temperature, top_k, max_new_tokens):
|
|
"""Generate audio using BiCodec (Spark-TTS)."""
|
|
prompt = "<|task_tts|><|start_content|>" + text + "<|end_content|><|start_global_token|>"
|
|
inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
|
|
generated = model.generate(
|
|
**inputs,
|
|
max_new_tokens = max_new_tokens,
|
|
do_sample = True,
|
|
temperature = temperature,
|
|
top_k = top_k,
|
|
eos_token_id = tokenizer.eos_token_id,
|
|
pad_token_id = tokenizer.pad_token_id,
|
|
)
|
|
new_tokens = generated[:, inputs.input_ids.shape[1] :]
|
|
decoded_text = tokenizer.batch_decode(new_tokens, skip_special_tokens = False)[0]
|
|
return self._audio_codec_manager.decode_bicodec(decoded_text, str(model.device))
|
|
|
|
def _generate_dac(
|
|
self,
|
|
model,
|
|
tokenizer,
|
|
text,
|
|
temperature,
|
|
top_k,
|
|
top_p,
|
|
min_p,
|
|
max_new_tokens,
|
|
repetition_penalty,
|
|
):
|
|
"""Generate audio using DAC (OuteTTS). Follows Oute_TTS_(1B).ipynb exactly."""
|
|
# Monkey-patch RepetitionPenaltyLogitsProcessor with a 64-token window
|
|
# (same as the OuteTTS notebook) to avoid degenerate repetition.
|
|
self._patch_repetition_penalty_processor()
|
|
|
|
prompt = (
|
|
"<|im_start|>\n<|text_start|>"
|
|
+ text
|
|
+ "<|text_end|>\n<|audio_start|><|global_features_start|>\n"
|
|
)
|
|
|
|
with torch.inference_mode():
|
|
# Derive the autocast device from the loaded model, not from the
|
|
# global backend: a CPU-fallback DAC on an XPU/CUDA host must not
|
|
# open a GPU autocast context around CPU tensors.
|
|
device_type = (
|
|
model.device.type
|
|
if hasattr(model.device, "type")
|
|
else str(model.device).split(":", 1)[0]
|
|
)
|
|
# Clamp to autocast-supported backends so exotic devices
|
|
# (e.g. "meta" during accelerate offloaded loading) do not raise.
|
|
# MPS is autocast-supported since torch 2.3, keep it in the set.
|
|
if device_type not in ("cuda", "xpu", "mps", "cpu"):
|
|
device_type = "cpu"
|
|
# CPU and XPU autocast only accept bfloat16/float16. For a
|
|
# float32 model, skip autocast entirely to avoid raising or
|
|
# producing a warning on every generate call.
|
|
autocast_dtype_supported = model.dtype in (torch.bfloat16, torch.float16)
|
|
if device_type in ("cpu", "xpu") and not autocast_dtype_supported:
|
|
autocast_ctx = contextlib.nullcontext()
|
|
else:
|
|
autocast_ctx = torch.amp.autocast(device_type, dtype = model.dtype)
|
|
with autocast_ctx:
|
|
inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
|
|
generated = model.generate(
|
|
**inputs,
|
|
temperature = temperature,
|
|
top_k = top_k,
|
|
top_p = top_p,
|
|
min_p = min_p,
|
|
repetition_penalty = repetition_penalty,
|
|
max_new_tokens = max_new_tokens,
|
|
)
|
|
decoded_text = tokenizer.batch_decode(generated, skip_special_tokens = False)[0]
|
|
return self._audio_codec_manager.decode_dac(decoded_text, str(model.device))
|
|
|
|
_repetition_penalty_patched = False
|
|
|
|
@classmethod
|
|
def _patch_repetition_penalty_processor(cls):
|
|
"""Monkey-patch transformers' RepetitionPenaltyLogitsProcessor with a
|
|
64-token sliding-window variant (from the OuteTTS notebook).
|
|
Applied once per process.
|
|
"""
|
|
if cls._repetition_penalty_patched:
|
|
return
|
|
cls._repetition_penalty_patched = True
|
|
|
|
from transformers import LogitsProcessor
|
|
import transformers.generation.utils as generation_utils
|
|
|
|
class RepetitionPenaltyLogitsProcessorPatch(LogitsProcessor):
|
|
def __init__(self, penalty: float):
|
|
self.penalty_last_n = 64
|
|
if not isinstance(penalty, float) or penalty <= 0:
|
|
raise ValueError(f"`penalty` has to be a positive float, but is {penalty}")
|
|
self.penalty = penalty
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self, input_ids: torch.LongTensor, scores: torch.FloatTensor
|
|
) -> torch.FloatTensor:
|
|
if self.penalty_last_n == 0 or self.penalty == 1.0:
|
|
return scores
|
|
batch_size, seq_len = input_ids.shape
|
|
vocab_size = scores.shape[-1]
|
|
for b in range(batch_size):
|
|
start_index = max(0, seq_len - self.penalty_last_n)
|
|
window_indices = input_ids[b, start_index:]
|
|
if window_indices.numel() == 0:
|
|
continue
|
|
for token_id in set(window_indices.tolist()):
|
|
if token_id >= vocab_size:
|
|
continue
|
|
logit = scores[b, token_id]
|
|
scores[b, token_id] = (
|
|
logit * self.penalty if logit <= 0 else logit / self.penalty
|
|
)
|
|
return scores
|
|
|
|
generation_utils.RepetitionPenaltyLogitsProcessor = RepetitionPenaltyLogitsProcessorPatch
|
|
logger.info("Patched RepetitionPenaltyLogitsProcessor with 64-token window for OuteTTS")
|
|
|
|
def _apply_chat_template_for_generation(
|
|
self,
|
|
tokenizer,
|
|
messages: list,
|
|
*,
|
|
tools: Optional[list] = None,
|
|
enable_thinking: Optional[bool] = None,
|
|
reasoning_effort: Optional[str] = None,
|
|
preserve_thinking: Optional[bool] = None,
|
|
) -> str:
|
|
"""Render the chat prompt, peeling kwargs the template doesn't
|
|
understand. Delegates to the dependency-light helper module so the
|
|
fallback chain is unit-testable without pulling unsloth / torch into
|
|
the test sandbox.
|
|
"""
|
|
from core.inference.chat_template_helpers import (
|
|
apply_chat_template_for_generation,
|
|
)
|
|
return apply_chat_template_for_generation(
|
|
tokenizer,
|
|
messages,
|
|
tools = tools,
|
|
enable_thinking = enable_thinking,
|
|
reasoning_effort = reasoning_effort,
|
|
preserve_thinking = preserve_thinking,
|
|
)
|
|
|
|
def format_chat_prompt(
|
|
self,
|
|
messages: list,
|
|
system_prompt: str = None,
|
|
) -> str:
|
|
if not self.active_model_name or self.active_model_name not in self.models:
|
|
logger.error("No active model available")
|
|
return ""
|
|
|
|
if self.models[self.active_model_name].get("tokenizer") is None:
|
|
logger.error("Tokenizer not loaded for active model")
|
|
return ""
|
|
|
|
chat_template_info = self.models[self.active_model_name].get("chat_template_info", {})
|
|
tokenizer = self.models[self.active_model_name]["tokenizer"]
|
|
tokenizer = getattr(tokenizer, "tokenizer", tokenizer)
|
|
|
|
chat_messages = []
|
|
|
|
if system_prompt:
|
|
chat_messages.append({"role": "system", "content": system_prompt})
|
|
|
|
last_role = "system" if system_prompt else None
|
|
|
|
for msg in messages:
|
|
role = msg.get("role", "")
|
|
content = content_to_text(msg.get("content", ""))
|
|
|
|
if role in ["system", "user", "assistant"] and content.strip():
|
|
if role == last_role:
|
|
logger.debug(f"Skipping consecutive {role} message to maintain alternation")
|
|
continue
|
|
|
|
if role == "user":
|
|
import re
|
|
clean_content = re.sub(r"<[^>]+>", "", content).strip()
|
|
if clean_content:
|
|
chat_messages.append({"role": role, "content": clean_content})
|
|
last_role = role
|
|
elif role == "assistant" and content.strip():
|
|
chat_messages.append({"role": role, "content": content})
|
|
last_role = role
|
|
elif role == "system":
|
|
continue
|
|
|
|
if chat_messages and chat_messages[-1]["role"] == "assistant":
|
|
logger.debug("Removing final assistant message to ensure proper alternation")
|
|
chat_messages.pop()
|
|
|
|
logger.info(f"Sending {len(chat_messages)} messages to tokenizer:")
|
|
for i, msg in enumerate(chat_messages):
|
|
logger.info(f" {i}: {msg['role']} - {msg['content'][:50]}...")
|
|
|
|
try:
|
|
formatted_prompt = tokenizer.apply_chat_template(
|
|
chat_messages, tokenize = False, add_generation_prompt = True
|
|
)
|
|
logger.info(f"Successfully applied tokenizer's native chat template")
|
|
return formatted_prompt
|
|
except Exception as e:
|
|
error_msg = str(e).lower()
|
|
if "chat_template is not set" in error_msg or "no template argument" in error_msg:
|
|
logger.info(
|
|
f"Base model detected - no built-in chat template available, using fallback formatting"
|
|
)
|
|
else:
|
|
logger.warning(f"Failed to apply tokenizer chat template: {e}")
|
|
logger.debug(
|
|
f"""Failed with messages: {[f"{m['role']}: {m['content'][:30]}..." for m in chat_messages]}"""
|
|
)
|
|
|
|
if chat_template_info.get("has_template", False):
|
|
logger.info("Falling back to manual template formatting based on detected patterns")
|
|
template_type = chat_template_info.get("format_type", "generic")
|
|
manual_prompt = self._format_chat_manual(
|
|
chat_messages,
|
|
template_type,
|
|
chat_template_info.get("special_tokens", {}),
|
|
)
|
|
logger.info(f"Manual template result: {manual_prompt[:200]}...")
|
|
return manual_prompt
|
|
else:
|
|
logger.info("Using generic chat formatting for base model")
|
|
return self._format_generic_template(chat_messages, {})
|
|
|
|
def _format_chat_manual(self, messages: list, template_type: str, special_tokens: dict) -> str:
|
|
"""Manual chat-formatting fallback when the tokenizer template fails.
|
|
|
|
Args:
|
|
messages: List of message dictionaries
|
|
template_type: Detected template type
|
|
special_tokens: Dictionary of special tokens
|
|
|
|
Returns:
|
|
str: Manually formatted prompt
|
|
"""
|
|
if template_type == "llama3":
|
|
return self._format_llama3_template(messages, special_tokens)
|
|
elif template_type == "mistral":
|
|
return self._format_mistral_template(messages, special_tokens)
|
|
elif template_type == "chatml":
|
|
return self._format_chatml_template(messages, special_tokens)
|
|
elif template_type == "alpaca":
|
|
return self._format_alpaca_template(messages, special_tokens)
|
|
else:
|
|
return self._format_generic_template(messages, special_tokens)
|
|
|
|
def _format_llama3_template(self, messages: list, special_tokens: dict) -> str:
|
|
"""Format messages using Llama 3 template"""
|
|
bos_token = special_tokens.get("bos_token", "<|begin_of_text|>")
|
|
formatted = bos_token
|
|
|
|
for msg in messages:
|
|
role = msg["role"]
|
|
content = content_to_text(msg["content"])
|
|
formatted += f"<|start_header_id|>{role}<|end_header_id|>\n\n{content}<|eot_id|>"
|
|
|
|
formatted += "<|start_header_id|>assistant<|end_header_id|>\n\n"
|
|
return formatted
|
|
|
|
def _format_mistral_template(self, messages: list, special_tokens: dict) -> str:
|
|
"""Format messages using Mistral template"""
|
|
bos_token = special_tokens.get("bos_token", "<s>")
|
|
formatted = bos_token
|
|
|
|
system_msg = None
|
|
conversation = []
|
|
|
|
for msg in messages:
|
|
if msg["role"] == "system":
|
|
system_msg = content_to_text(msg["content"])
|
|
else:
|
|
conversation.append(msg)
|
|
|
|
i = 0
|
|
while i < len(conversation):
|
|
if conversation[i]["role"] == "user":
|
|
user_content = content_to_text(conversation[i]["content"])
|
|
|
|
if system_msg and i == 0:
|
|
user_content = f"{system_msg}\n\n{user_content}"
|
|
|
|
formatted += f"[INST] {user_content} [/INST]"
|
|
|
|
if i + 1 < len(conversation) and conversation[i + 1]["role"] == "assistant":
|
|
formatted += f" {content_to_text(conversation[i + 1]['content'])}</s>"
|
|
i += 2
|
|
else:
|
|
formatted += " "
|
|
break
|
|
else:
|
|
i += 1
|
|
|
|
return formatted
|
|
|
|
def _format_chatml_template(self, messages: list, special_tokens: dict) -> str:
|
|
"""Format messages using ChatML template"""
|
|
formatted = ""
|
|
|
|
for msg in messages:
|
|
role = msg["role"]
|
|
content = content_to_text(msg["content"])
|
|
formatted += f"<|im_start|>{role}\n{content}<|im_end|>\n"
|
|
|
|
formatted += "<|im_start|>assistant\n"
|
|
return formatted
|
|
|
|
def _format_alpaca_template(self, messages: list, special_tokens: dict) -> str:
|
|
"""Format messages using Alpaca template"""
|
|
formatted = ""
|
|
system_msg = None
|
|
|
|
for msg in messages:
|
|
content = content_to_text(msg["content"])
|
|
if msg["role"] == "system":
|
|
system_msg = content
|
|
elif msg["role"] == "user":
|
|
if system_msg:
|
|
formatted += f"### Instruction:\n{system_msg}\n\n### Input:\n{content}\n\n### Response:\n"
|
|
system_msg = None
|
|
else:
|
|
formatted += f"### Human:\n{content}\n\n### Assistant:\n"
|
|
elif msg["role"] == "assistant":
|
|
formatted += f"{content}\n\n"
|
|
|
|
return formatted
|
|
|
|
def _format_generic_template(self, messages: list, special_tokens: dict) -> str:
|
|
"""Generic fallback formatting"""
|
|
formatted = ""
|
|
|
|
for msg in messages:
|
|
role = msg["role"].title()
|
|
content = content_to_text(msg["content"])
|
|
formatted += f"{role}: {content}\n"
|
|
|
|
formatted += "Assistant: "
|
|
return formatted
|
|
|
|
def check_vision_model_compatibility(self) -> bool:
|
|
"""Whether the current model supports vision."""
|
|
current_model = self.get_current_model()
|
|
if current_model and current_model in self.models:
|
|
return self.models[current_model].get("is_vision", False)
|
|
return False
|
|
|
|
def _reset_model_generation_state(self, model_name: str):
|
|
"""Reset generation state for a specific model to prevent contamination."""
|
|
if model_name not in self.models:
|
|
return
|
|
|
|
model = self.models[model_name].get("model")
|
|
if not model:
|
|
return
|
|
|
|
try:
|
|
# Common pattern for Unsloth/Hugging Face models
|
|
if hasattr(model, "past_key_values"):
|
|
model.past_key_values = None
|
|
if hasattr(model, "generation_config"):
|
|
if hasattr(model.generation_config, "past_key_values"):
|
|
model.generation_config.past_key_values = None
|
|
|
|
logger.debug(f"Reset generation state for model: {model_name}")
|
|
except Exception as e:
|
|
logger.warning(f"Could not fully reset model state for {model_name}: {e}")
|
|
|
|
def reset_generation_state(self):
|
|
"""Reset any cached generation state to prevent hanging after errors"""
|
|
try:
|
|
# Clear cached state for ALL loaded models
|
|
for model_name in self.models.keys():
|
|
self._reset_model_generation_state(model_name)
|
|
|
|
clear_gpu_cache()
|
|
logger.debug("Cleared GPU cache")
|
|
|
|
import gc
|
|
|
|
gc.collect()
|
|
logger.info("Performed comprehensive generation state reset")
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Could not fully reset generation state: {e}")
|
|
|
|
def resize_image(
|
|
self,
|
|
img,
|
|
max_size: int = 800,
|
|
):
|
|
"""Resize image while maintaining aspect ratio if either dimension exceeds max_size"""
|
|
if img is None:
|
|
return None
|
|
if img.size[0] > max_size or img.size[1] > max_size:
|
|
from PIL import Image
|
|
|
|
ratio = min(max_size / img.size[0], max_size / img.size[1])
|
|
new_size = (int(img.size[0] * ratio), int(img.size[1] * ratio))
|
|
return img.resize(new_size, Image.Resampling.LANCZOS)
|
|
return img
|
|
|
|
def _generation_stop_token_ids(self, model, generation_kwargs: dict):
|
|
"""Return the stop-token ids active for a ``generate`` call."""
|
|
if "eos_token_id" in generation_kwargs:
|
|
return generation_kwargs.get("eos_token_id")
|
|
generation_config = getattr(model, "generation_config", None)
|
|
eos_token_id = getattr(generation_config, "eos_token_id", None)
|
|
if eos_token_id is not None:
|
|
return eos_token_id
|
|
config = getattr(model, "config", None)
|
|
return getattr(config, "eos_token_id", None)
|
|
|
|
def _cancel_stopping_criteria(self, cancel_event):
|
|
"""Build a Transformers stopping criteria list for user cancellation."""
|
|
if cancel_event is None:
|
|
return None
|
|
from transformers.generation.stopping_criteria import (
|
|
StoppingCriteria,
|
|
StoppingCriteriaList,
|
|
)
|
|
|
|
class _CancelCriteria(StoppingCriteria):
|
|
def __init__(self, ev):
|
|
self.ev = ev
|
|
|
|
def __call__(self, input_ids, scores, **kwargs):
|
|
return self.ev.is_set()
|
|
|
|
return StoppingCriteriaList([_CancelCriteria(cancel_event)])
|
|
|
|
def _clean_generated_text(
|
|
self,
|
|
text: str,
|
|
*,
|
|
stop_token_ids = None,
|
|
) -> str:
|
|
"""Strip leaked response-boundary tokens after streaming."""
|
|
if self._is_gpt_oss_model():
|
|
# HarmonyTextStreamer emits clean <think>...</think>. Strip any
|
|
# harmony protocol tokens and other gpt-oss tokens (e.g.
|
|
# <|return|>) that leak past the streamer.
|
|
import re
|
|
text = re.sub(r"<\|[a-z_]+\|>", "", text)
|
|
return text.strip()
|
|
|
|
tokenizer = self.models.get(self.active_model_name, {}).get("tokenizer")
|
|
tokenizer = getattr(tokenizer, "tokenizer", tokenizer)
|
|
if tokenizer:
|
|
if stop_token_ids is None:
|
|
stop_token_ids = self.models.get(self.active_model_name, {}).get(
|
|
"chat_turn_end_eos_ids"
|
|
)
|
|
if isinstance(stop_token_ids, int):
|
|
stop_token_ids = (stop_token_ids,)
|
|
for token_id in stop_token_ids or ():
|
|
try:
|
|
token = tokenizer.convert_ids_to_tokens(int(token_id))
|
|
except Exception:
|
|
token = None
|
|
if isinstance(token, str) and token and text.endswith(token):
|
|
text = text[: -len(token)]
|
|
elif (
|
|
isinstance(token, str)
|
|
and token
|
|
and text.endswith("</think>")
|
|
and text[: -len("</think>")].endswith(token)
|
|
):
|
|
text = text[: -len("</think>") - len(token)] + "</think>"
|
|
return text.strip()
|
|
|
|
def _load_chat_template_info(self, model_name: str):
|
|
if model_name not in self.models or not self.models[model_name].get("tokenizer"):
|
|
return
|
|
|
|
tokenizer = self.models[model_name]["tokenizer"]
|
|
chat_template_info = {
|
|
"has_template": False,
|
|
"template": None,
|
|
"format_type": "generic",
|
|
"special_tokens": {},
|
|
"template_name": None,
|
|
}
|
|
|
|
try:
|
|
from utils.datasets import MODEL_TO_TEMPLATE_MAPPER
|
|
|
|
# Exact match first
|
|
model_name_lower = model_name.lower()
|
|
if model_name_lower in MODEL_TO_TEMPLATE_MAPPER:
|
|
chat_template_info["template_name"] = MODEL_TO_TEMPLATE_MAPPER[model_name_lower]
|
|
logger.info(
|
|
f"Detected template '{chat_template_info['template_name']}' for {model_name} from mapper"
|
|
)
|
|
else:
|
|
# Partial match (for variants like model_name-bnb-4bit)
|
|
for key in MODEL_TO_TEMPLATE_MAPPER:
|
|
if key in model_name_lower or model_name_lower in key:
|
|
chat_template_info["template_name"] = MODEL_TO_TEMPLATE_MAPPER[key]
|
|
logger.info(
|
|
f"Detected template '{chat_template_info['template_name']}' for {model_name} (partial match)"
|
|
)
|
|
break
|
|
except Exception as e:
|
|
logger.warning(f"Could not detect template from mapper for {model_name}: {e}")
|
|
|
|
try:
|
|
if hasattr(tokenizer, "chat_template") and tokenizer.chat_template:
|
|
chat_template_info["has_template"] = True
|
|
chat_template_info["template"] = tokenizer.chat_template
|
|
|
|
template_str = tokenizer.chat_template.lower()
|
|
|
|
if "start_header_id" in template_str and "end_header_id" in template_str:
|
|
chat_template_info["format_type"] = "llama3"
|
|
elif "[inst]" in template_str and "[/inst]" in template_str:
|
|
chat_template_info["format_type"] = "mistral"
|
|
elif "<|im_start|>" in template_str and "<|im_end|>" in template_str:
|
|
chat_template_info["format_type"] = "chatml"
|
|
elif "### instruction:" in template_str or "### human:" in template_str:
|
|
chat_template_info["format_type"] = "alpaca"
|
|
else:
|
|
chat_template_info["format_type"] = "custom"
|
|
|
|
logger.info(
|
|
f"Loaded chat template for {model_name} (detected as {chat_template_info['format_type']} format)"
|
|
)
|
|
logger.debug(f"Template preview: {tokenizer.chat_template[:200]}...")
|
|
|
|
special_tokens = {}
|
|
if hasattr(tokenizer, "bos_token") and tokenizer.bos_token:
|
|
special_tokens["bos_token"] = tokenizer.bos_token
|
|
if hasattr(tokenizer, "eos_token") and tokenizer.eos_token:
|
|
special_tokens["eos_token"] = tokenizer.eos_token
|
|
if hasattr(tokenizer, "pad_token") and tokenizer.pad_token:
|
|
special_tokens["pad_token"] = tokenizer.pad_token
|
|
|
|
chat_template_info["special_tokens"] = special_tokens
|
|
|
|
else:
|
|
logger.info(f"No chat template found for {model_name}, will use generic formatting")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error loading chat template info for {model_name}: {e}")
|
|
|
|
self.models[model_name]["chat_template_info"] = chat_template_info
|
|
|
|
if chat_template_info["has_template"]:
|
|
logger.info(
|
|
f"Chat template loaded for {model_name}: {chat_template_info['format_type']} format"
|
|
)
|
|
else:
|
|
logger.info(f"No built-in chat template for {model_name}, will use generic formatting")
|
|
|
|
def get_current_model(self) -> Optional[str]:
|
|
"""Currently active model name."""
|
|
return self.active_model_name
|
|
|
|
def is_model_loading(self) -> bool:
|
|
"""Whether any model is currently loading."""
|
|
return len(self.loading_models) > 0
|
|
|
|
def get_loading_model(self) -> Optional[str]:
|
|
"""Name of the currently loading model."""
|
|
return next(iter(self.loading_models)) if self.loading_models else None
|
|
|
|
def load_model_simple(
|
|
self,
|
|
model_path: str,
|
|
hf_token: Optional[str] = None,
|
|
max_seq_length: int = 2048,
|
|
load_in_4bit: bool = True,
|
|
) -> bool:
|
|
"""Simple model-loading wrapper for the chat interface. Takes a string
|
|
path and builds the ModelConfig internally.
|
|
|
|
Args:
|
|
model_path: Model name or path (e.g., "unsloth/llama-3-8b")
|
|
hf_token: HuggingFace token for gated models
|
|
max_seq_length: Maximum sequence length
|
|
load_in_4bit: Whether to use 4-bit quantization
|
|
|
|
Returns:
|
|
bool: True if successful, False otherwise
|
|
"""
|
|
try:
|
|
config = ModelConfig.from_ui_selection(
|
|
model_path,
|
|
lora_path = None, # No LoRA for chat
|
|
is_lora = False,
|
|
)
|
|
|
|
return self.load_model(
|
|
config = config,
|
|
max_seq_length = max_seq_length,
|
|
dtype = None, # Auto-detect
|
|
load_in_4bit = load_in_4bit,
|
|
hf_token = hf_token,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in load_model_simple: {e}")
|
|
return False
|
|
|
|
|
|
# Global inference backend instance
|
|
inference_backend = InferenceBackend()
|
|
|
|
|
|
def get_inference_backend() -> InferenceBackend:
|
|
return inference_backend
|