.

.gitignore

@@ -19,6 +19,7 @@ ref_audios
 tools/AP_BWE/24kto48k/*
 !tools/AP_BWE/24kto48k/readme.txt
 onnx_export
+compile_cache
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

GPT_SoVITS/Accelerate/PyTorch/backends/flash_attn_varlen_cuda_graph.py

@@ -114,6 +114,9 @@ class T2SDecoder(T2SDecoderABC):
 
         self.kv_class = KVCacheNHD
 
+    def compile(self, *args, **kwds):
+        pass
+
     def post_forward(self, idx: int, session: T2SSession) -> None:
         return super().post_forward(idx, session)
 
@@ -133,6 +136,8 @@ class CUDAGraphCache(CUDAGraphCacheABC):
         if session.id == self.id:
             self.assigned = False
         else:
+            assert session.graph
+            session.graph.reset()
             del session.graph, session.xy_pos_, session.xy_dec_, session.input_pos, session.kv_cache
 
     def get_cache_graph(self, session: T2SSession):

GPT_SoVITS/Accelerate/PyTorch/backends/sage_attn_varlen_cuda_graph.py

@@ -107,6 +107,9 @@ class T2SDecoder(T2SDecoderABC):
 
         self.kv_class = KVCacheHND
 
+    def compile(self, *args, **kwds):
+        pass
+
     def pre_forward(self, session: T2SSession) -> tuple[list[Tensor], dict[str, Tensor]]:
         return list(), dict(cu_seqlens_q=session.cu_seqlens_q, cu_seqlens_kv=session.cu_seqlens_kv)
 
@@ -136,6 +139,8 @@ class CUDAGraphCache(CUDAGraphCacheABC):
         if session.id == self.id:
             self.assigned = False
         else:
+            assert session.graph
+            session.graph.reset()
             del (
                 session.graph,
                 session.xy_pos_,

GPT_SoVITS/Accelerate/PyTorch/backends/torch_static_cuda_graph.py

@@ -130,6 +130,8 @@ class CUDAGraphCache(CUDAGraphCacheABC):
         if session.id == self.id:
             self.assigned = False
         else:
+            assert session.graph
+            session.graph.reset()
             del (
                 session.graph,
                 session.xy_pos_,

GPT_SoVITS/Accelerate/PyTorch/t2s_engine.py

@@ -32,7 +32,7 @@ class T2SEngine(T2SEngineProtocol):
         self.dtype = dtype
 
         self.decoder_model: T2SDecoderABC = decoder_model.to(self.device, self.dtype)
-        self.decoder_model.compile()
+        # self.decoder_model.compile()
 
         self.graphcache: CUDAGraphCacheABC = self.init_cache()
 

GPT_SoVITS/Accelerate/PyTorch/t2s_model_abc.py

@@ -6,9 +6,12 @@ from __future__ import annotations
 
 import math
 import os
+import pickle
+import platform
 import random
 from abc import ABC, abstractmethod
 from contextlib import nullcontext
+from pathlib import Path
 from typing import MutableSequence
 
 import torch
@@ -17,6 +20,8 @@ import torch.nn.functional as F
 from torch.cuda.graphs import CUDAGraph
 from torch.profiler import ProfilerAction, tensorboard_trace_handler
 
+from tools.my_utils import get_machine_id
+
 from . import nn
 from .structs import KVCacheProtocol, T2SDecoderProtocol, T2SSession
 
@@ -453,6 +458,7 @@ class T2SDecoderABC(nn.Module, ABC, T2SDecoderProtocol):
         self.kv_class: type[KVCacheABC]
 
         self.GraphCache: CUDAGraphCacheABC | None
+        self.compiled: bool = False
 
         self.ar_text_embedding = TokenEmbedding(self.embedding_dim, self.phoneme_vocab_size)
         self.ar_text_position = SinePositionalEmbedding(
@@ -517,14 +523,61 @@ class T2SDecoderABC(nn.Module, ABC, T2SDecoderProtocol):
         return xy_pos
 
     def compile(self, *args, **kwds):
-        # Experimental features to reduce compilation times, will be on by default in future
-        torch._inductor.config.triton.cudagraph_skip_dynamic_graphs = True
-        torch._inductor.config.coordinate_descent_tuning = True
-        torch._inductor.config.triton.unique_kernel_names = True
-        torch._inductor.config.fx_graph_cache = True
-        torch._inductor.config.triton.cudagraph_trees = True
-        torch._inductor.config.triton.cudagraph_support_input_mutation = True
-        self.h.compile(fullgraph=True, mode="reduce-overhead")
+        if (
+            torch.cuda.is_available()
+            and platform.system() != "Windows"
+            or platform.system() == "macOS"
+            and self.compiled is False
+        ):
+            cache_path = Path.cwd() / "compile_cache"
+            if cache_path.exists() is False:
+                cache_path.mkdir(parents=True, exist_ok=True)
+            else:
+                assert cache_path.is_dir()
+                cache_file = (
+                    cache_path
+                    / f"t2s_decoder_{self.n_layer}_{self.hidden_dim}_{self.n_head}_{self.ffn_dim}_{self.phoneme_vocab_size}_{get_machine_id()}_{torch.__version__}.GSV"
+                )
+                if cache_file.exists():
+                    try:
+                        with open(cache_file, "rb") as f:
+                            cache_data = pickle.load(f)
+                        torch.compiler.load_cache_artifacts(cache_data)
+                    except Exception as e:
+                        print(f"Failed to resotore compile cache from {cache_file}: {e}")
+
+            # Experimental features to reduce compilation times, will be on by default in future
+            torch._inductor.config.triton.cudagraph_skip_dynamic_graphs = True
+            torch._inductor.config.coordinate_descent_tuning = True
+            torch._inductor.config.triton.unique_kernel_names = True
+            torch._inductor.config.fx_graph_cache = True
+            torch._inductor.config.triton.cudagraph_trees = True
+            torch._inductor.config.triton.cudagraph_support_input_mutation = True
+            self.h.compile(fullgraph=True, mode="reduce-overhead")
+            self.compiled = True
+
+    def save_compile_cache(self):
+        if torch.cuda.is_available() and platform.system() != "Windows" or platform.system() == "macOS":
+            cache_path = Path.cwd() / "compile_cache"
+            if cache_path.exists() is False:
+                cache_path.mkdir(parents=True, exist_ok=True)
+            else:
+                assert cache_path.is_dir()
+                cache_file = (
+                    cache_path
+                    / f"t2s_decoder_{self.n_layer}_{self.hidden_dim}_{self.n_head}_{self.ffn_dim}_{self.phoneme_vocab_size}_{get_machine_id()}_{torch.__version__}.GSV"
+                )
+                if cache_file.exists():
+                    return
+
+                try:
+                    cache = torch.compiler.save_cache_artifacts()
+                    assert cache
+                    cache_data = cache[0]
+                    with open(cache_file, "wb") as f:
+                        pickle.dump(cache_data, f)
+                except Exception as e:
+                    print(f"Failed to save compile cache to {cache_file}: {e}")
 
     def capture(
         self, input_pos: Tensor, x: Tensor, x_dec: Tensor, kv_caches: MutableSequence[KVCacheProtocol], *args, **kwds

tools/my_utils.py

@@ -1,7 +1,10 @@
 import ctypes
+import hashlib
 import io
 import os
+import platform
 import sys
+import uuid
 from pathlib import Path
 from typing import IO, Union
 
@@ -354,3 +357,11 @@ class _open_file(_opener[IO[bytes]]):
 
     def __exit__(self, *args):
         self.file_like.close()
+
+
+def get_machine_id():
+    mac = uuid.getnode()
+    hostname = platform.node()
+    raw = f"{mac}-{hostname}"
+    serial = hashlib.md5(raw.encode()).hexdigest()[:20]  # 取前20位
+    return serial