mirror of
https://github.com/remsky/Kokoro-FastAPI.git
synced 2025-04-13 09:39:17 +00:00
Refactor: Consolidate PyTorch CPU and GPU backends into a single PyTorchBackend class; remove obsolete files
This commit is contained in:
remsky
parent
3547d95ee6
commit
00497f8872
4 changed files with 258 additions and 426 deletions
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@ -4,8 +4,7 @@ from .base import BaseModelBackend
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from .model_manager import ModelManager, get_manager
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from .onnx_cpu import ONNXCPUBackend
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from .onnx_gpu import ONNXGPUBackend
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from .pytorch_cpu import PyTorchCPUBackend
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from .pytorch_gpu import PyTorchGPUBackend
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from .pytorch_backend import PyTorchBackend
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__all__ = [
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'BaseModelBackend',
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@ -13,6 +12,5 @@ __all__ = [
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'get_manager',
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'ONNXCPUBackend',
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'ONNXGPUBackend',
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'PyTorchCPUBackend',
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'PyTorchGPUBackend',
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'PyTorchBackend',
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]
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@ -12,8 +12,7 @@ from ..core.model_config import ModelConfig, model_config
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from .base import BaseModelBackend
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from .onnx_cpu import ONNXCPUBackend
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from .onnx_gpu import ONNXGPUBackend
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from .pytorch_cpu import PyTorchCPUBackend
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from .pytorch_gpu import PyTorchGPUBackend
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from .pytorch_backend import PyTorchBackend
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from .session_pool import CPUSessionPool, StreamingSessionPool
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@ -63,18 +62,18 @@ class ModelManager:
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self._current_backend = 'onnx_gpu'
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logger.info("Initialized new ONNX GPU backend")
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else:
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self._backends['pytorch_gpu'] = PyTorchGPUBackend()
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self._current_backend = 'pytorch_gpu'
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logger.info("Initialized new PyTorch GPU backend")
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self._backends['pytorch'] = PyTorchBackend()
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self._current_backend = 'pytorch'
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logger.info("Initialized new PyTorch backend on GPU")
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else:
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if settings.use_onnx:
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self._backends['onnx_cpu'] = ONNXCPUBackend()
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self._current_backend = 'onnx_cpu'
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logger.info("Initialized new ONNX CPU backend")
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else:
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self._backends['pytorch_cpu'] = PyTorchCPUBackend()
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self._current_backend = 'pytorch_cpu'
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logger.info("Initialized new PyTorch CPU backend")
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self._backends['pytorch'] = PyTorchBackend()
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self._current_backend = 'pytorch'
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logger.info("Initialized new PyTorch backend on CPU")
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# Initialize locks for each backend
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for backend in self._backends:
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@ -95,10 +94,10 @@ class ModelManager:
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"""
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try:
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# Determine backend type based on settings
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if settings.use_gpu and torch.cuda.is_available():
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backend_type = 'pytorch_gpu' if not settings.use_onnx else 'onnx_gpu'
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if settings.use_onnx:
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backend_type = 'onnx_gpu' if settings.use_gpu and torch.cuda.is_available() else 'onnx_cpu'
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else:
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backend_type = 'pytorch_cpu' if not settings.use_onnx else 'onnx_cpu'
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backend_type = 'pytorch'
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# Get backend
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backend = self.get_backend(backend_type)
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@ -167,13 +166,10 @@ class ModelManager:
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Returns:
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Backend type to use
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"""
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has_gpu = settings.use_gpu and torch.cuda.is_available()
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# If ONNX is preferred or model is ONNX format
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if settings.use_onnx or model_path.lower().endswith('.onnx'):
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return 'onnx_gpu' if has_gpu else 'onnx_cpu'
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else:
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return 'pytorch_gpu' if has_gpu else 'pytorch_cpu'
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return 'onnx_gpu' if settings.use_gpu and torch.cuda.is_available() else 'onnx_cpu'
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return 'pytorch'
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async def load_model(
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self,
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@ -1,225 +1,244 @@
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"""GPU-based PyTorch inference backend."""
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import gc
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from typing import Optional
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import numpy as np
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import torch
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from loguru import logger
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from ..builds.models import build_model
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from ..core import paths
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from ..core.model_config import model_config
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from .base import BaseModelBackend
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class CUDAStreamManager:
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"""CUDA stream manager."""
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def __init__(self, num_streams: int):
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"""Initialize stream manager.
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Args:
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num_streams: Number of CUDA streams
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"""
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self.streams = [torch.cuda.Stream() for _ in range(num_streams)]
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self._current = 0
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def get_next_stream(self) -> torch.cuda.Stream:
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"""Get next available stream.
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Returns:
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CUDA stream
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"""
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stream = self.streams[self._current]
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self._current = (self._current + 1) % len(self.streams)
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return stream
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@torch.no_grad()
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def forward(
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model: torch.nn.Module,
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tokens: list[int],
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ref_s: torch.Tensor,
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speed: float,
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stream: Optional[torch.cuda.Stream] = None
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) -> np.ndarray:
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"""Forward pass through model.
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Args:
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model: PyTorch model
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tokens: Input tokens
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ref_s: Reference signal (shape: [1, n_features])
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speed: Speed multiplier
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stream: Optional CUDA stream
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Returns:
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Generated audio
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"""
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device = ref_s.device
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# Use provided stream or default
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with torch.cuda.stream(stream) if stream else torch.cuda.default_stream():
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# Initial tensor setup
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tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
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input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
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text_mask = length_to_mask(input_lengths).to(device)
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# Split reference signals (style_dim=128 from config)
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style_dim = 128
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s_ref = ref_s[:, :style_dim].clone().to(device)
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s_content = ref_s[:, style_dim:].clone().to(device)
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# BERT and encoder pass
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bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
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d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
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# Predictor forward pass
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d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask)
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x, _ = model.predictor.lstm(d)
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# Duration prediction
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duration = model.predictor.duration_proj(x)
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duration = torch.sigmoid(duration).sum(axis=-1) / speed
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pred_dur = torch.round(duration).clamp(min=1).long()
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del duration, x
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# Alignment matrix construction
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pred_aln_trg = torch.zeros(input_lengths.item(), pred_dur.sum().item(), device=device)
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c_frame = 0
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for i in range(pred_aln_trg.size(0)):
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pred_aln_trg[i, c_frame:c_frame + pred_dur[0, i].item()] = 1
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c_frame += pred_dur[0, i].item()
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pred_aln_trg = pred_aln_trg.unsqueeze(0)
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# Matrix multiplications
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en = d.transpose(-1, -2) @ pred_aln_trg
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del d
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F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content)
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del en
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# Final text encoding and decoding
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t_en = model.text_encoder(tokens, input_lengths, text_mask)
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asr = t_en @ pred_aln_trg
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del t_en
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# Generate output
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output = model.decoder(asr, F0_pred, N_pred, s_ref)
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# Ensure operation completion if using custom stream
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if stream:
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stream.synchronize()
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return output.squeeze().cpu().numpy()
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def length_to_mask(lengths: torch.Tensor) -> torch.Tensor:
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"""Create attention mask from lengths."""
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max_len = lengths.max()
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mask = torch.arange(max_len, device=lengths.device)[None, :].expand(lengths.shape[0], -1)
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if lengths.dtype != mask.dtype:
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mask = mask.to(dtype=lengths.dtype)
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return mask + 1 > lengths[:, None]
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class PyTorchGPUBackend(BaseModelBackend):
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"""PyTorch GPU inference backend."""
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def __init__(self):
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"""Initialize GPU backend."""
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super().__init__()
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from ..core.config import settings
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if not (settings.use_gpu and torch.cuda.is_available()):
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raise RuntimeError("GPU backend requires GPU support and use_gpu=True")
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self._device = "cuda" # Device is enforced by backend selection in model_manager
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self._model: Optional[torch.nn.Module] = None
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# Configure GPU settings
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config = model_config.pytorch_gpu
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if config.sync_cuda:
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torch.cuda.synchronize()
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torch.cuda.set_device(config.device_id)
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# Initialize stream manager
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self._stream_manager = CUDAStreamManager(config.cuda_streams)
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async def load_model(self, path: str) -> None:
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"""Load PyTorch model.
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Args:
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path: Path to model file
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Raises:
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RuntimeError: If model loading fails
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"""
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try:
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# Get verified model path
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model_path = await paths.get_model_path(path)
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logger.info(f"Loading PyTorch model: {model_path}")
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self._model = await build_model(model_path, self._device)
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except Exception as e:
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raise RuntimeError(f"Failed to load PyTorch model: {e}")
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def generate(
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self,
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tokens: list[int],
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voice: torch.Tensor,
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speed: float = 1.0
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) -> np.ndarray:
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"""Generate audio using GPU model.
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Args:
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tokens: Input token IDs
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voice: Voice embedding tensor
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speed: Speed multiplier
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Returns:
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Generated audio samples
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Raises:
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RuntimeError: If generation fails
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"""
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if not self.is_loaded:
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raise RuntimeError("Model not loaded")
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try:
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# Check memory and cleanup if needed
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if self._check_memory():
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self._clear_memory()
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# Get reference style from voice pack
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ref_s = voice[len(tokens)].clone().to(self._device)
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if ref_s.dim() == 1:
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ref_s = ref_s.unsqueeze(0) # Add batch dimension if needed
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# Get next available stream
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stream = self._stream_manager.get_next_stream()
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# Generate audio using stream
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return forward(self._model, tokens, ref_s, speed, stream)
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except Exception as e:
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logger.error(f"Generation failed: {e}")
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if model_config.pytorch_gpu.retry_on_oom and "out of memory" in str(e).lower():
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self._clear_memory()
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return self.generate(tokens, voice, speed) # Retry once
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raise
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finally:
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if model_config.pytorch_gpu.sync_cuda:
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torch.cuda.synchronize()
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def _check_memory(self) -> bool:
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"""Check if memory usage is above threshold."""
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if torch.cuda.is_available():
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memory_gb = torch.cuda.memory_allocated() / 1e9
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return memory_gb > model_config.pytorch_gpu.memory_threshold
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return False
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def _clear_memory(self) -> None:
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"""Clear GPU memory."""
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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"""PyTorch inference backend with environment-based configuration."""
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import gc
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from typing import Optional
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from contextlib import nullcontext
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from typing import Optional
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import numpy as np
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import torch
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from loguru import logger
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from ..builds.models import build_model
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from ..core import paths
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from ..core.model_config import model_config
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from ..core.config import settings
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from .base import BaseModelBackend
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class CUDAStreamManager:
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"""CUDA stream manager for GPU operations."""
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def __init__(self, num_streams: int):
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"""Initialize stream manager.
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Args:
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num_streams: Number of CUDA streams
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"""
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self.streams = [torch.cuda.Stream() for _ in range(num_streams)]
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self._current = 0
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def get_next_stream(self) -> torch.cuda.Stream:
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"""Get next available stream.
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Returns:
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CUDA stream
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"""
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stream = self.streams[self._current]
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self._current = (self._current + 1) % len(self.streams)
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return stream
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@torch.no_grad()
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def forward(
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model: torch.nn.Module,
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tokens: list[int],
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ref_s: torch.Tensor,
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speed: float,
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stream: Optional[torch.cuda.Stream] = None,
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) -> np.ndarray:
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"""Forward pass through model.
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Args:
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model: PyTorch model
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tokens: Input tokens
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ref_s: Reference signal
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speed: Speed multiplier
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stream: Optional CUDA stream (GPU only)
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Returns:
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Generated audio
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"""
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device = ref_s.device
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# Use provided stream or default for GPU
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context = (
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torch.cuda.stream(stream) if stream and device.type == "cuda" else nullcontext()
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)
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with context:
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# Initial tensor setup
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tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
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input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
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text_mask = length_to_mask(input_lengths).to(device)
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# Split reference signals
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style_dim = 128
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s_ref = ref_s[:, :style_dim].clone().to(device)
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s_content = ref_s[:, style_dim:].clone().to(device)
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# BERT and encoder pass
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bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
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d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
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# Predictor forward pass
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d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask)
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x, _ = model.predictor.lstm(d)
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# Duration prediction
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duration = model.predictor.duration_proj(x)
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duration = torch.sigmoid(duration).sum(axis=-1) / speed
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pred_dur = torch.round(duration).clamp(min=1).long()
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del duration, x
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# Alignment matrix construction
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pred_aln_trg = torch.zeros(
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input_lengths.item(), pred_dur.sum().item(), device=device
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)
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c_frame = 0
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for i in range(pred_aln_trg.size(0)):
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pred_aln_trg[i, c_frame : c_frame + pred_dur[0, i].item()] = 1
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c_frame += pred_dur[0, i].item()
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pred_aln_trg = pred_aln_trg.unsqueeze(0)
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# Matrix multiplications
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en = d.transpose(-1, -2) @ pred_aln_trg
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del d
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F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content)
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del en
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# Final text encoding and decoding
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t_en = model.text_encoder(tokens, input_lengths, text_mask)
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asr = t_en @ pred_aln_trg
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del t_en
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# Generate output
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output = model.decoder(asr, F0_pred, N_pred, s_ref)
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# Ensure operation completion if using custom stream
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if stream and device.type == "cuda":
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stream.synchronize()
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return output.squeeze().cpu().numpy()
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def length_to_mask(lengths: torch.Tensor) -> torch.Tensor:
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"""Create attention mask from lengths."""
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max_len = lengths.max()
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mask = torch.arange(max_len, device=lengths.device)[None, :].expand(
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lengths.shape[0], -1
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)
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if lengths.dtype != mask.dtype:
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mask = mask.to(dtype=lengths.dtype)
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return mask + 1 > lengths[:, None]
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class PyTorchBackend(BaseModelBackend):
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"""PyTorch inference backend with environment-based configuration."""
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def __init__(self):
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"""Initialize backend based on environment configuration."""
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super().__init__()
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# Configure device based on settings
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self._device = (
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"cuda" if settings.use_gpu and torch.cuda.is_available() else "cpu"
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)
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self._model: Optional[torch.nn.Module] = None
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# Apply device-specific configurations
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if self._device == "cuda":
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config = model_config.pytorch_gpu
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if config.sync_cuda:
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torch.cuda.synchronize()
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torch.cuda.set_device(config.device_id)
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self._stream_manager = CUDAStreamManager(config.cuda_streams)
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else:
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config = model_config.pytorch_cpu
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if config.num_threads > 0:
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torch.set_num_threads(config.num_threads)
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if config.pin_memory:
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torch.set_default_tensor_type(torch.FloatTensor)
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async def load_model(self, path: str) -> None:
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"""Load PyTorch model.
|
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|
||||
Args:
|
||||
path: Path to model file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If model loading fails
|
||||
"""
|
||||
try:
|
||||
# Get verified model path
|
||||
model_path = await paths.get_model_path(path)
|
||||
|
||||
logger.info(f"Loading PyTorch model on {self._device}: {model_path}")
|
||||
self._model = await build_model(model_path, self._device)
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load PyTorch model: {e}")
|
||||
|
||||
def generate(
|
||||
self, tokens: list[int], voice: torch.Tensor, speed: float = 1.0
|
||||
) -> np.ndarray:
|
||||
"""Generate audio using model.
|
||||
|
||||
Args:
|
||||
tokens: Input token IDs
|
||||
voice: Voice embedding tensor
|
||||
speed: Speed multiplier
|
||||
|
||||
Returns:
|
||||
Generated audio samples
|
||||
|
||||
Raises:
|
||||
RuntimeError: If generation fails
|
||||
"""
|
||||
if not self.is_loaded:
|
||||
raise RuntimeError("Model not loaded")
|
||||
|
||||
try:
|
||||
# Memory management for GPU
|
||||
if self._device == "cuda":
|
||||
if self._check_memory():
|
||||
self._clear_memory()
|
||||
stream = self._stream_manager.get_next_stream()
|
||||
else:
|
||||
stream = None
|
||||
|
||||
# Get reference style from voice pack
|
||||
ref_s = voice[len(tokens)].clone().to(self._device)
|
||||
if ref_s.dim() == 1:
|
||||
ref_s = ref_s.unsqueeze(0)
|
||||
|
||||
# Generate audio
|
||||
return forward(self._model, tokens, ref_s, speed, stream)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Generation failed: {e}")
|
||||
if (
|
||||
self._device == "cuda"
|
||||
and model_config.pytorch_gpu.retry_on_oom
|
||||
and "out of memory" in str(e).lower()
|
||||
):
|
||||
self._clear_memory()
|
||||
return self.generate(tokens, voice, speed)
|
||||
raise
|
||||
finally:
|
||||
if self._device == "cuda" and model_config.pytorch_gpu.sync_cuda:
|
||||
torch.cuda.synchronize()
|
||||
|
||||
def _check_memory(self) -> bool:
|
||||
"""Check if memory usage is above threshold."""
|
||||
if self._device == "cuda":
|
||||
memory_gb = torch.cuda.memory_allocated() / 1e9
|
||||
return memory_gb > model_config.pytorch_gpu.memory_threshold
|
||||
return False
|
||||
|
||||
def _clear_memory(self) -> None:
|
||||
"""Clear device memory."""
|
||||
if self._device == "cuda":
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
|
@ -1,181 +0,0 @@
|
|||
"""CPU-based PyTorch inference backend."""
|
||||
|
||||
import gc
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from loguru import logger
|
||||
|
||||
from ..builds.models import build_model
|
||||
from ..core import paths
|
||||
from ..core.model_config import model_config
|
||||
from .base import BaseModelBackend
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(model: torch.nn.Module, tokens: list[int], ref_s: torch.Tensor, speed: float) -> np.ndarray:
|
||||
"""Forward pass through model with memory management.
|
||||
|
||||
Args:
|
||||
model: PyTorch model
|
||||
tokens: Input tokens
|
||||
ref_s: Reference signal
|
||||
speed: Speed multiplier
|
||||
|
||||
Returns:
|
||||
Generated audio
|
||||
"""
|
||||
device = ref_s.device
|
||||
pred_aln_trg = None
|
||||
asr = None
|
||||
|
||||
try:
|
||||
# Initial tensor setup
|
||||
tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
|
||||
# Split reference signals
|
||||
s_content = ref_s[:, 128:].clone().to(device)
|
||||
s_ref = ref_s[:, :128].clone().to(device)
|
||||
|
||||
# BERT and encoder pass
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
# Predictor forward pass
|
||||
d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask)
|
||||
x, _ = model.predictor.lstm(d)
|
||||
|
||||
# Duration prediction
|
||||
duration = model.predictor.duration_proj(x)
|
||||
duration = torch.sigmoid(duration).sum(axis=-1) / speed
|
||||
pred_dur = torch.round(duration).clamp(min=1).long()
|
||||
del duration, x # Free large intermediates
|
||||
|
||||
# Alignment matrix construction
|
||||
pred_aln_trg = torch.zeros(
|
||||
input_lengths.item(),
|
||||
pred_dur.sum().item(),
|
||||
device=device
|
||||
)
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + pred_dur[0, i].item()] = 1
|
||||
c_frame += pred_dur[0, i].item()
|
||||
pred_aln_trg = pred_aln_trg.unsqueeze(0)
|
||||
|
||||
# Matrix multiplications with cleanup
|
||||
en = d.transpose(-1, -2) @ pred_aln_trg
|
||||
del d
|
||||
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content)
|
||||
del en
|
||||
|
||||
# Final text encoding and decoding
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
asr = t_en @ pred_aln_trg
|
||||
del t_en
|
||||
|
||||
# Generate output
|
||||
output = model.decoder(asr, F0_pred, N_pred, s_ref)
|
||||
result = output.squeeze().cpu().numpy()
|
||||
|
||||
return result
|
||||
|
||||
finally:
|
||||
# Clean up largest tensors if they were created
|
||||
if pred_aln_trg is not None:
|
||||
del pred_aln_trg
|
||||
if asr is not None:
|
||||
del asr
|
||||
|
||||
|
||||
def length_to_mask(lengths: torch.Tensor) -> torch.Tensor:
|
||||
"""Create attention mask from lengths.
|
||||
|
||||
Args:
|
||||
lengths: Sequence lengths
|
||||
|
||||
Returns:
|
||||
Boolean mask tensor
|
||||
"""
|
||||
max_len = lengths.max()
|
||||
mask = torch.arange(max_len, device=lengths.device)[None, :].expand(
|
||||
lengths.shape[0], -1
|
||||
)
|
||||
if lengths.dtype != mask.dtype:
|
||||
mask = mask.to(dtype=lengths.dtype)
|
||||
return mask + 1 > lengths[:, None]
|
||||
|
||||
|
||||
class PyTorchCPUBackend(BaseModelBackend):
|
||||
"""PyTorch CPU inference backend."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize CPU backend."""
|
||||
super().__init__()
|
||||
self._device = "cpu"
|
||||
self._model: Optional[torch.nn.Module] = None
|
||||
|
||||
# Configure PyTorch CPU settings
|
||||
config = model_config.pytorch_cpu
|
||||
if config.num_threads > 0:
|
||||
torch.set_num_threads(config.num_threads)
|
||||
if config.pin_memory:
|
||||
torch.set_default_tensor_type(torch.FloatTensor)
|
||||
|
||||
async def load_model(self, path: str) -> None:
|
||||
"""Load PyTorch model.
|
||||
|
||||
Args:
|
||||
path: Path to model file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If model loading fails
|
||||
"""
|
||||
try:
|
||||
# Get verified model path
|
||||
model_path = await paths.get_model_path(path)
|
||||
|
||||
logger.info(f"Loading PyTorch model on CPU: {model_path}")
|
||||
self._model = await build_model(model_path, self._device)
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load PyTorch model: {e}")
|
||||
|
||||
def generate(
|
||||
self,
|
||||
tokens: list[int],
|
||||
voice: torch.Tensor,
|
||||
speed: float = 1.0
|
||||
) -> np.ndarray:
|
||||
"""Generate audio using CPU model.
|
||||
|
||||
Args:
|
||||
tokens: Input token IDs
|
||||
voice: Voice embedding tensor
|
||||
speed: Speed multiplier
|
||||
|
||||
Returns:
|
||||
Generated audio samples
|
||||
|
||||
Raises:
|
||||
RuntimeError: If generation fails
|
||||
"""
|
||||
if not self.is_loaded:
|
||||
raise RuntimeError("Model not loaded")
|
||||
|
||||
try:
|
||||
# Prepare input
|
||||
ref_s = voice[len(tokens)].clone()
|
||||
|
||||
# Generate audio
|
||||
return forward(self._model, tokens, ref_s, speed)
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Generation failed: {e}")
|
||||
finally:
|
||||
# Clean up memory
|
||||
gc.collect()
|
||||
Loading…
Add table
Reference in a new issue