fix(core): mps infer incorrect on some device (fix #444)

ChatTTS/core.py

@@ -3,7 +3,6 @@
 import tempfile
 from dataclasses import dataclass
 from typing import Literal, Optional, List, Callable, Tuple, Dict
-from functools import lru_cache
 from json import load
 from pathlib import Path
 
@@ -13,9 +12,8 @@
 from omegaconf import OmegaConf
 from vocos import Vocos
 from huggingface_hub import snapshot_download
-from transformers.generation import TopKLogitsWarper, TopPLogitsWarper
 
-from .model import DVAE, GPT, CustomRepetitionPenaltyLogitsProcessorRepeat
+from .model import DVAE, GPT, gen_logits
 from .utils import (
     check_all_assets,
     download_all_assets,
@@ -72,10 +70,10 @@ def download_models(
     ) -> Optional[str]:
         if source == "local":
             download_path = os.getcwd()
-            if not check_all_assets(self.sha256_map, update=True) or force_redownload:
+            if not check_all_assets(Path(download_path), self.sha256_map, update=True) or force_redownload:
                 with tempfile.TemporaryDirectory() as tmp:
                     download_all_assets(tmpdir=tmp)
-                if not check_all_assets(self.sha256_map, update=False):
+                if not check_all_assets(Path(download_path), self.sha256_map, update=False):
                     self.logger.error(
                         "download to local path %s failed.", download_path
                     )
@@ -108,7 +106,7 @@ def download_models(
                 return None
         elif source == "custom":
             self.logger.log(logging.INFO, f"try to load from local: {custom_path}")
-            if not check_all_assets(self.sha256_map, update=False, base_dir=Path(custom_path)):
+            if not check_all_assets(Path(custom_path), self.sha256_map, update=False):
                 self.logger.error(
                     "check models in custom path %s failed.", custom_path
                 )
@@ -147,7 +145,6 @@ def unload(self):
         self.normalizer.destroy()
         del self.normalizer
         del self.sha256_map
-        self._gen_logits.cache_clear()
         del_list = ["vocos", "_vocos_decode", "gpt", "decoder", "dvae"]
         for module in del_list:
             if hasattr(self, module):
@@ -415,30 +412,6 @@ def _gen_gpt_inputs(self, text: str, device="cpu"):
 
         return input_ids, text_token, text_mask
 
-    @lru_cache
-    def _gen_logits(
-        self,
-        num_code: int,
-        top_P=0.7,
-        top_K=20,
-        repetition_penalty=1.0,
-    ):
-        logits_warpers = []
-        if top_P is not None:
-            logits_warpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3))
-        if top_K is not None:
-            logits_warpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3))
-
-        logits_processors = []
-        if repetition_penalty is not None and repetition_penalty != 1:
-            logits_processors.append(
-                CustomRepetitionPenaltyLogitsProcessorRepeat(
-                    repetition_penalty, num_code, 16
-                )
-            )
-
-        return logits_warpers, logits_processors
-
     def _apply_spk_emb(
         self,
         emb: torch.Tensor,
@@ -494,7 +467,7 @@ def _infer_code(
 
         num_code = int(gpt.emb_code[0].num_embeddings - 1)
 
-        logits_warpers, logits_processors = self._gen_logits(
+        logits_warpers, logits_processors = gen_logits(
             num_code=num_code,
             top_P=params.top_P,
             top_K=params.top_K,
@@ -519,6 +492,8 @@ def _infer_code(
 
         del_all(text_token)
         del emb, text_token, input_ids
+        del_all(logits_warpers)
+        del_all(logits_processors)
 
         return result
 
@@ -539,7 +514,7 @@ def _refine_text(
 
         input_ids, text_token, text_mask = self._gen_gpt_inputs(text, gpt.device_gpt)
 
-        logits_warpers, logits_processors = self._gen_logits(
+        logits_warpers, logits_processors = gen_logits(
             num_code=len(tokenizer),
             top_P=params.top_P,
             top_K=params.top_K,
@@ -568,5 +543,7 @@ def _refine_text(
 
         del_all(text_token)
         del emb, text_token, input_ids
+        del_all(logits_warpers)
+        del_all(logits_processors)
 
         return next(result)

ChatTTS/model/__init__.py

@@ -1,3 +1,3 @@
 from .dvae import DVAE
 from .gpt import GPT
-from .processors import CustomRepetitionPenaltyLogitsProcessorRepeat
+from .processors import gen_logits

ChatTTS/model/processors.py

@@ -1,5 +1,6 @@
 import torch
 import torch.nn.functional as F
+from transformers.generation import TopKLogitsWarper, TopPLogitsWarper
 
 
 class CustomRepetitionPenaltyLogitsProcessorRepeat:
@@ -31,24 +32,24 @@ def __call__(
         del inp, oth, scores, con, alpha
         return out
 
+def gen_logits(
+    num_code: int,
+    top_P=0.7,
+    top_K=20,
+    repetition_penalty=1.0,
+):
+    logits_warpers = []
+    if top_P is not None:
+        logits_warpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3))
+    if top_K is not None:
+        logits_warpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3))
+
+    logits_processors = []
+    if repetition_penalty is not None and repetition_penalty != 1:
+        logits_processors.append(
+            CustomRepetitionPenaltyLogitsProcessorRepeat(
+                repetition_penalty, num_code, 16
+            )
+        )
 
-"""class CustomRepetitionPenaltyLogitsProcessor():
-
-    def __init__(self, penalty: float, max_input_ids: int, past_window: int):
-        if not isinstance(penalty, float) or not (penalty > 0):
-            raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
-
-        self.penalty = penalty
-        self.max_input_ids = max_input_ids
-        self.past_window = past_window
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-
-        input_ids = input_ids[:, -self.past_window:]
-        score = torch.gather(scores, 1, input_ids)
-        _score = score.detach().clone()
-        score = torch.where(score < 0, score * self.penalty, score / self.penalty)
-        score[input_ids>=self.max_input_ids] = _score[input_ids>=self.max_input_ids]
-        scores.scatter_(1, input_ids, score)
-
-        return scores"""
+    return logits_warpers, logits_processors

ChatTTS/utils/dl.py

@@ -42,7 +42,7 @@ def check_model(
     return True
 
 
-def check_all_assets(sha256_map: dict[str, str], update=False, base_dir = Path(os.getcwd())) -> bool:
+def check_all_assets(base_dir: Path, sha256_map: dict[str, str], update=False) -> bool:
     logger.get_logger().info("checking assets...")
     current_dir = base_dir / "asset"
     names = [