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"""
Unified model loading utility supporting ModelScope, HuggingFace and local path loading
"""
import os
import logging
import threading
from typing import Optional, Dict, Any, Tuple
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from funasr_detach import AutoModel
# Global cache for downloaded models to avoid repeated downloads
# Key: (model_path, source)
# Value: local_model_path
_model_download_cache = {}
_download_cache_lock = threading.Lock()
class ModelSource:
"""Model source enumeration"""
MODELSCOPE = "modelscope"
HUGGINGFACE = "huggingface"
LOCAL = "local"
AUTO = "auto" # Auto-detect
class UnifiedModelLoader:
"""Unified model loader"""
def __init__(self):
self.logger = logging.getLogger(__name__)
def _cached_snapshot_download(self, model_path: str, source: str, **kwargs) -> str:
"""
Cached version of snapshot_download to avoid repeated downloads
Args:
model_path: Model path or ID to download
source: Model source ('modelscope' or 'huggingface')
**kwargs: Additional arguments for snapshot_download
Returns:
Local path to downloaded model
"""
cache_key = (model_path, source, str(sorted(kwargs.items())))
# Check cache first
with _download_cache_lock:
if cache_key in _model_download_cache:
cached_path = _model_download_cache[cache_key]
self.logger.info(f"Using cached download for {model_path} from {source}: {cached_path}")
return cached_path
# Cache miss, need to download
if source == ModelSource.MODELSCOPE:
from modelscope.hub.snapshot_download import snapshot_download
local_path = snapshot_download(model_path, **kwargs)
elif source == ModelSource.HUGGINGFACE:
from huggingface_hub import snapshot_download
local_path = snapshot_download(model_path, **kwargs)
else:
raise ValueError(f"Unsupported source for cached download: {source}")
# Cache the result
with _download_cache_lock:
_model_download_cache[cache_key] = local_path
self.logger.info(f"Downloaded and cached {model_path} from {source}: {local_path}")
return local_path
def detect_model_source(self, model_path: str) -> str:
"""
Automatically detect model source
Args:
model_path: Model path or ID
Returns:
Model source type
"""
# Local path detection
if os.path.exists(model_path) or os.path.isabs(model_path):
return ModelSource.LOCAL
# ModelScope format detection (usually includes username/model_name)
if "/" in model_path and not model_path.startswith("http"):
# If contains modelscope keyword or is known modelscope format
if "modelscope" in model_path.lower() or self._is_modelscope_format(model_path):
return ModelSource.MODELSCOPE
else:
# Default to HuggingFace
return ModelSource.HUGGINGFACE
return ModelSource.LOCAL
def _is_modelscope_format(self, model_path: str) -> bool:
"""Detect if it's ModelScope format model ID"""
# Can be judged according to known ModelScope model ID formats
# For example: iic/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online
modelscope_patterns = []
return any(pattern in model_path for pattern in modelscope_patterns)
def _prepare_quantization_config(self, quantization_config: Optional[str], torch_dtype: Optional[torch.dtype] = None) -> Tuple[Dict[str, Any], bool]:
"""
Prepare quantization configuration for model loading
Args:
quantization_config: Quantization type ('int4', 'int8', or None)
torch_dtype: PyTorch data type for compute operations
Returns:
Tuple of (quantization parameters dict, should_set_torch_dtype)
"""
if not quantization_config:
return {}, True
quantization_config = quantization_config.lower()
if quantization_config == "int8":
# Use user-specified torch_dtype for compute, default to bfloat16
compute_dtype = torch_dtype if torch_dtype is not None else torch.bfloat16
self.logger.info(f"🔧 INT8 quantization: using {compute_dtype} for compute operations")
bnb_config = BitsAndBytesConfig(
load_in_8bit=True,
bnb_8bit_compute_dtype=compute_dtype,
)
return {
"quantization_config": bnb_config
}, False # INT8 quantization handles data types automatically, don't set torch_dtype
elif quantization_config == "int4":
# Use user-specified torch_dtype for compute, default to bfloat16
compute_dtype = torch_dtype if torch_dtype is not None else torch.bfloat16
self.logger.info(f"🔧 INT4 quantization: using {compute_dtype} for compute operations")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=True,
)
return {
"quantization_config": bnb_config
}, False # INT4 quantization handles torch_dtype internally, don't set it again
else:
raise ValueError(f"Unsupported quantization config: {quantization_config}. Supported: 'int4', 'int8'")
def load_transformers_model(
self,
model_path: str,
source: str = ModelSource.AUTO,
quantization_config: Optional[str] = None,
**kwargs
) -> Tuple:
"""
Load Transformers model (for StepAudioTTS)
Args:
model_path: Model path or ID
source: Model source, auto means auto-detect
quantization_config: Quantization configuration ('int4', 'int8', or None for no quantization)
**kwargs: Other parameters (torch_dtype, device_map, etc.)
Returns:
(model, tokenizer) tuple
"""
if source == ModelSource.AUTO:
source = self.detect_model_source(model_path)
self.logger.info(f"Loading Transformers model from {source}: {model_path}")
if quantization_config:
self.logger.info(f"🔧 {quantization_config.upper()} quantization enabled")
# Prepare quantization configuration
quantization_kwargs, should_set_torch_dtype = self._prepare_quantization_config(quantization_config, kwargs.get("torch_dtype"))
try:
if source == ModelSource.LOCAL:
# Local loading
load_kwargs = {
"device_map": kwargs.get("device_map", "auto"),
"trust_remote_code": True,
"local_files_only": True
}
# Add quantization configuration if specified
load_kwargs.update(quantization_kwargs)
# Add torch_dtype based on quantization requirements
if should_set_torch_dtype and kwargs.get("torch_dtype") is not None:
load_kwargs["torch_dtype"] = kwargs.get("torch_dtype")
# Standard loading
model = AutoModelForCausalLM.from_pretrained(
model_path,
**load_kwargs
)
tokenizer = AutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True,
local_files_only=True
)
elif source == ModelSource.MODELSCOPE:
# Load from ModelScope
from modelscope import AutoModelForCausalLM as MSAutoModelForCausalLM
from modelscope import AutoTokenizer as MSAutoTokenizer
model_path = self._cached_snapshot_download(model_path, ModelSource.MODELSCOPE)
load_kwargs = {
"device_map": kwargs.get("device_map", "auto"),
"trust_remote_code": True,
"local_files_only": True
}
# Add quantization configuration if specified
load_kwargs.update(quantization_kwargs)
# Add torch_dtype based on quantization requirements
if should_set_torch_dtype and kwargs.get("torch_dtype") is not None:
load_kwargs["torch_dtype"] = kwargs.get("torch_dtype")
# Standard loading
model = MSAutoModelForCausalLM.from_pretrained(
model_path,
**load_kwargs
)
tokenizer = MSAutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True,
local_files_only=True
)
elif source == ModelSource.HUGGINGFACE:
model_path = self._cached_snapshot_download(model_path, ModelSource.HUGGINGFACE)
# Load from HuggingFace
load_kwargs = {
"device_map": kwargs.get("device_map", "auto"),
"trust_remote_code": True,
"local_files_only": True
}
# Add quantization configuration if specified
load_kwargs.update(quantization_kwargs)
# Add torch_dtype based on quantization requirements
if should_set_torch_dtype and kwargs.get("torch_dtype") is not None:
load_kwargs["torch_dtype"] = kwargs.get("torch_dtype")
# Standard loading
model = AutoModelForCausalLM.from_pretrained(
model_path,
**load_kwargs
)
tokenizer = AutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True,
local_files_only=True
)
else:
raise ValueError(f"Unsupported model source: {source}")
self.logger.info(f"Successfully loaded model from {source}")
return model, tokenizer, model_path
except Exception as e:
self.logger.error(f"Failed to load model from {source}: {e}")
raise
def load_funasr_model(
self,
repo_path: str,
model_path: str,
source: str = ModelSource.AUTO,
**kwargs
) -> AutoModel:
"""
Load FunASR model (for StepAudioTokenizer)
Args:
model_path: Model path or ID
source: Model source, auto means auto-detect
**kwargs: Other parameters
Returns:
FunASR AutoModel instance
"""
if source == ModelSource.AUTO:
source = self.detect_model_source(model_path)
self.logger.info(f"Loading FunASR model from {source}: {model_path}")
try:
# Extract model_revision to avoid duplicate passing
model_revision = kwargs.pop("model_revision", "main")
# Map ModelSource to model_hub parameter
if source == ModelSource.LOCAL:
model_hub = "local"
elif source == ModelSource.MODELSCOPE:
model_hub = "ms"
elif source == ModelSource.HUGGINGFACE:
model_hub = "hf"
else:
raise ValueError(f"Unsupported model source: {source}")
# Use unified download_model for all cases
model = AutoModel(
repo_path=repo_path,
model=model_path,
model_hub=model_hub,
model_revision=model_revision,
**kwargs
)
self.logger.info(f"Successfully loaded FunASR model from {source}")
return model
except Exception as e:
self.logger.error(f"Failed to load FunASR model from {source}: {e}")
raise
def resolve_model_path(
self,
base_path: str,
model_name: str,
source: str = ModelSource.AUTO
) -> str:
"""
Resolve model path
Args:
base_path: Base path
model_name: Model name
source: Model source
Returns:
Resolved model path
"""
if source == ModelSource.AUTO:
# First check local path
local_path = os.path.join(base_path, model_name)
if os.path.exists(local_path):
return local_path
# If local doesn't exist, return model name for online download
return model_name
elif source == ModelSource.LOCAL:
return os.path.join(base_path, model_name)
else:
# For online sources, directly return model name/ID
return model_name
# Global instance
model_loader = UnifiedModelLoader() |