Fixed an issue with having fairseq installed at all will brick logging

setup.py

@@ -45,7 +45,7 @@ setup(
         "encodec>=0.1.1",
         "phonemizer>=2.1.0",
         "matplotlib>=3.6.0",
-        "numpy>=1.23.3",
+        "numpy==1.23.0",
         "omegaconf==2.0.6",
         "tqdm>=4.64.1",
         "humanize>=4.4.0",
@@ -58,8 +58,7 @@ setup(
         "auraloss[all]",
         "vocos",
         "h5py",
-        "git+https://github.com/microsoft/torchscale",
-        "fairseq",
+        "torchscale @ git+https://github.com/microsoft/torchscale",
     ],
     url="https://git.ecker.tech/mrq/vall-e",
 )

vall_e/models/ar.py

@@ -66,24 +66,7 @@ class AR(Base):
 				shift_targ_list=True,
 				return_all_resp=False,
 			)
-		else:
-			return self._generate(
-				text_list,
-				proms_list,
-				max_steps,
-				sampling_temperature,
 
-				naive=naive,
-			)
-
-	def _generate(
-		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
-		max_steps: int,
-		sampling_temperature: float,
-		naive: bool = True,
-	):
 		device = text_list[0].device
 		resp_list: list[Tensor] = [
 			torch.zeros(0, device=device).to(torch.int16) for _ in text_list

vall_e/models/retnet.py

@@ -1,5 +1,54 @@
-from fairseq.models import FairseqIncrementalDecoder
-from fairseq.incremental_decoding_utils import with_incremental_state
+"""
+# https://github.com/facebookresearch/fairseq/blob/main/fairseq/incremental_decoding_utils.py
+# Copied directly because even having fairseq installed WILL break logging, why are corposhitters like this
+"""
+
+import uuid
+from typing import Dict, Optional
+
+from torch import Tensor
+
+class FairseqIncrementalState(object):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.init_incremental_state()
+
+    def init_incremental_state(self):
+        self._incremental_state_id = str(uuid.uuid4())
+
+    def _get_full_incremental_state_key(self, key: str) -> str:
+        return "{}.{}".format(self._incremental_state_id, key)
+
+    def get_incremental_state(
+        self,
+        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
+        key: str,
+    ) -> Optional[Dict[str, Optional[Tensor]]]:
+        """Helper for getting incremental state for an nn.Module."""
+        full_key = self._get_full_incremental_state_key(key)
+        if incremental_state is None or full_key not in incremental_state:
+            return None
+        return incremental_state[full_key]
+
+    def set_incremental_state(
+        self,
+        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
+        key: str,
+        value: Dict[str, Optional[Tensor]],
+    ) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
+        """Helper for setting incremental state for an nn.Module."""
+        if incremental_state is not None:
+            full_key = self._get_full_incremental_state_key(key)
+            incremental_state[full_key] = value
+        return incremental_state
+
+
+def with_incremental_state(cls):
+    cls.__bases__ = (FairseqIncrementalState,) + tuple(
+        b for b in cls.__bases__ if b != FairseqIncrementalState
+    )
+    return cls
+
 
 from torchscale.architecture.config import RetNetConfig
 from torchscale.architecture.retnet import RetNetDecoder as Decoder

vall_e/train.py

@@ -83,13 +83,13 @@ def load_engines():
 	return trainer.load_engines(engines, cfg)
 
 def main():
+	setup_logging(cfg.log_dir)
+
 	#dist.init_distributed(dist_backend=get_accelerator().communication_backend_name())
 	if not deepspeed._initialized_dist:
 		deepspeed._initialized_dist = True
 		deepspeed.init_distributed()
 
-	setup_logging(cfg.log_dir)
-
 	train_dl, subtrain_dl, val_dl = create_train_val_dataloader()
 
 	def train_feeder(engines, batch, name):

vall_e/utils/utils.py

@@ -85,7 +85,7 @@ def load_state_dict_non_strict(model, state_dict, logger=None):
 	model.load_state_dict(state_dict, strict=False)
 
 class TqdmLoggingHandler(logging.Handler):
-	def __init__(self, level=logging.NOTSET):
+	def __init__(self, level=logging.INFO):
 		super().__init__(level)
 
 	def emit(self, record):
@@ -93,7 +93,7 @@ class TqdmLoggingHandler(logging.Handler):
 			msg = self.format(record)
 			tqdm.write(msg)
 			self.flush()
-		except Exception:
+		except Exception as e:
 			self.handleError(record) 
 
 @global_leader_only
@@ -116,13 +116,13 @@ def setup_logging(log_dir: str | Path | None = "log", log_level="info"):
 		file_handler.setLevel(logging.DEBUG)
 		handlers.append(file_handler)
 
+
 	logging.basicConfig(
 		level=logging.getLevelName(log_level.upper()),
 		format="%(asctime)s - %(name)s - %(levelname)s - \n%(message)s",
 		handlers=handlers,
 	)
 
-
 @overload
 def tree_map(fn: Callable, x: list[T]) -> list[T]:
 	...