tweaks to try and get deepspeed quantized inferencing, validating bitsandbytes and deepspeed quantization, nothing seems to work

vall_e/config.py

@@ -322,6 +322,7 @@ class DeepSpeed:
 	zero_optimization_level: int = 0
 	use_compression_training: bool = False
 	compression_bits: int = 8
+	inferencing: bool = False
 
 	@cached_property
 	def ds_cfg(self):
@@ -363,7 +364,7 @@ class DeepSpeed:
 						"quantize_verbose": True,
 						"quantization_type": "symmetric",
 						"rounding": "nearest",
-						"quantize_weight_in_forward": True,
+						"quantize_weight_in_forward": cfg.trainer.weight_dtype.lower() != "float16", #  MoQ (quantize in optimization step) weight quantization is only supported for FP16
 						"fp16_mixed_quantize":{
 							"enabled": False,
 							"quantize_change_ratio": 1
@@ -377,6 +378,35 @@ class DeepSpeed:
 								"quantization_period": 0
 							},
 							"modules": [
+							#	"^.+?$"
+								"blocks", # for transformer-based models
+								"retnet", # for RetNets-based models
+							]
+						}
+					}
+				},
+				"activation_quantization": {
+					"shared_parameters":{
+						"enabled": True,
+						"quantizer_kernel": True,
+						"schedule_offset": 0,
+						"quantize_groups": 64,
+						"quantize_verbose": True,
+						"quantization_type": "symmetric",
+						"rounding": "nearest",
+						"quantize_weight_in_forward": cfg.trainer.weight_dtype.lower() != "float16", #  MoQ (quantize in optimization step) weight quantization is only supported for FP16
+						"fp16_mixed_quantize":{
+							"enabled": False,
+							"quantize_change_ratio": 1
+						}
+					},
+					"different_groups": {
+						"aq1": {
+							"params": {
+								"bits": self.compression_bits,
+							},
+							"modules": [
+							#	"^.+?$"
 								"blocks", # for transformer-based models
 								"retnet", # for RetNets-based models
 							]

vall_e/data.py

@@ -382,7 +382,7 @@ class Dataset(_Dataset):
 			resps = _load_quants(path)
 
 		spkr_group = self.get_speaker_group(path)
-		lang = self.lang_symmap[ self.get_language(spkr_group) ]
+		lang = torch.tensor([ self.lang_symmap[ self.get_language(spkr_group) ]]).to(torch.uint8)
 
 		# append additional prompts in an attempt to artifically increase lengths / offer new data
 		if cfg.experimental and cfg.dataset.max_resps > 1 and random.random() < cfg.dataset.p_resp_append:

vall_e/engines/__init__.py

@@ -90,7 +90,7 @@ def load_engines():
 			model.load_state_dict(state, strict=cfg.trainer.strict_loading)
 
 		# deepspeed inferencing
-		if backend == "local" and inferencing and deepspeed_available: #and sys.platform.startswith("win"):
+		if backend == "local" and inferencing and deepspeed_available and cfg.trainer.deepspeed.inferencing: #and sys.platform.startswith("win"):
 			engine_class = _Engine
 			model = deepspeed.init_inference(model=model, mp_size=1, replace_with_kernel_inject=True, dtype=dtype if not amp else torch.float32).module
 

vall_e/inference.py

@@ -36,7 +36,7 @@ class TTS():
 
 		if amp is None:
 			amp = cfg.inference.amp
-		if dtype is None:
+		if dtype is None or dtype == "auto":
 			dtype = cfg.inference.weight_dtype
 		if device is None:
 			device = cfg.device
@@ -64,7 +64,7 @@ class TTS():
 			
 			model.load_state_dict(state)
 
-			if deepspeed_available:
+			if cfg.inference.backend == "local" and deepspeed_available and cfg.trainer.deepspeed.inferencing:
 				model = deepspeed.init_inference(model=model, mp_size=1, replace_with_kernel_inject=True, dtype=dtype if not amp else torch.float32).module
 
 			return model
@@ -88,6 +88,7 @@ class TTS():
 		else:
 			self.load_models()
 
+		if self.dtype != torch.int8:
 			self.ar = self.ar.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
 			self.nar = self.nar.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
 

vall_e/models/__init__.py

@@ -23,7 +23,7 @@ def get_model(cfg):
 	)
 	model._cfg = cfg
 
-	print(f"{name} parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
+	print(f"{name} ({next(model.parameters()).dtype}): {sum(p.numel() for p in model.parameters() if p.requires_grad)} parameters")
 
 	return model
 

vall_e/models/base.py

@@ -421,7 +421,7 @@ class Base(nn.Module):
 			logits = [ logit[-1:] for logit in logits ]
 
 		devices = [ logit.device for logit in logits ]
-		logits = [ logit.cpu() for logit in logits ]
+		logits = [ logit.to(device="cpu", dtype=logit.dtype if logit.dtype != torch.float16 else torch.float32) for logit in logits ]
 
 		# perform repetition penalizing	
 		logits = [ reptition_penalize(logit, previous=resps[:, -1], factor=repetition_penalty, decay=repetition_penalty_decay) for logit, resps in zip( logits, resps_list ) ]

vall_e/utils/wrapper.py

@@ -14,7 +14,8 @@ if cfg.bitsandbytes.enabled:
 		Linear = bnb.nn.Linear8bitLt
 
 	if cfg.bitsandbytes.embedding:
-		Embedding = bnb.nn.StableEmbedding
+		Embedding = bnb.nn.modules.Embedding
+		"""
 		Embedding.forward = lambda self, input: ( self.norm(F.embedding(
 			input,
 			self.weight,
@@ -24,6 +25,7 @@ if cfg.bitsandbytes.enabled:
 			self.scale_grad_by_freq,
 			self.sparse,
 		)).to(self.weight.dtype) )
+		"""
 
 
 if cfg.bitsandbytes.enabled:
@@ -62,11 +64,6 @@ def autocast_forward( func ):
 	def wrapper( self, input, *args, **kwargs ):
 		with autocasts( input, [torch.int16, torch.int8, torch.uint8], torch.int32 ) as k:
 			return func( self, k, *args, **kwargs )
-		"""
-		if input.dtype == torch.int16 or input.dtype == torch.int8 or input.dtype == torch.uint8:
-			return func( self, input.to(torch.int32), *args, **kwargs )
-		return func( self, input, *args, **kwargs )
-		"""
 	return wrapper
 Embedding.forward = autocast_forward(Embedding.forward)