Merge pull request 'main' (#47) from ken11o2/tortoise-tts:main into main

Reviewed-on: mrq/tortoise-tts#47

tortoise/api.py

@@ -259,7 +259,8 @@ class TextToSpeech:
         unsqueeze_sample_batches=False,
         input_sample_rate=22050, output_sample_rate=24000,
         autoregressive_model_path=None, diffusion_model_path=None, vocoder_model=None, tokenizer_json=None,
-    ):
+#    ):
+        use_deepspeed=False):  # Add use_deepspeed parameter
         """
         Constructor
         :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
@@ -280,7 +281,8 @@ class TextToSpeech:
         self.output_sample_rate = output_sample_rate
         self.minor_optimizations = minor_optimizations
         self.unsqueeze_sample_batches = unsqueeze_sample_batches
-
+        self.use_deepspeed = use_deepspeed  # Store use_deepspeed as an instance variable
+        print(f'use_deepspeed api_debug {use_deepspeed}')
         # for clarity, it's simpler to split these up and just predicate them on requesting VRAM-consuming optimizations
         self.preloaded_tensors = minor_optimizations
         self.use_kv_cache = minor_optimizations
@@ -359,7 +361,7 @@ class TextToSpeech:
                                           heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
                                           train_solo_embeddings=False).cpu().eval()
         self.autoregressive.load_state_dict(torch.load(self.autoregressive_model_path))
-        self.autoregressive.post_init_gpt2_config(kv_cache=self.use_kv_cache)
+        self.autoregressive.post_init_gpt2_config(use_deepspeed=self.use_deepspeed, kv_cache=self.use_kv_cache)
         if self.preloaded_tensors:
             self.autoregressive = migrate_to_device( self.autoregressive, self.device )
 

tortoise/do_tts.py

@@ -14,6 +14,7 @@ if __name__ == '__main__':
     parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
                                                  'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='random')
     parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
+    parser.add_argument('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=True)
     parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
     parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
                                                       'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
@@ -37,8 +38,8 @@ if __name__ == '__main__':
 
 
     os.makedirs(args.output_path, exist_ok=True)
-
+    #print(f'use_deepspeed do_tts_debug {use_deepspeed}')
-    tts = TextToSpeech(models_dir=args.model_dir)
+    tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed)
 
     selected_voices = args.voice.split(',')
     for k, selected_voice in enumerate(selected_voices):

tortoise/models/autoregressive.py

@@ -352,7 +352,7 @@ class UnifiedVoice(nn.Module):
         for module in embeddings:
             module.weight.data.normal_(mean=0.0, std=.02)
 
-    def post_init_gpt2_config(self, kv_cache=False):
+    def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False):
         seq_length = self.max_mel_tokens + self.max_text_tokens + 2
         gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
                                 n_positions=seq_length,
@@ -363,6 +363,17 @@ class UnifiedVoice(nn.Module):
                                 gradient_checkpointing=False,
                                 use_cache=True)
         self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head, kv_cache=kv_cache)
+        #print(f'use_deepspeed autoregressive_debug {use_deepspeed}')
+        if use_deepspeed and torch.cuda.is_available():
+            import deepspeed
+            self.ds_engine = deepspeed.init_inference(model=self.inference_model,  
+                                                    mp_size=1,
+                                                    replace_with_kernel_inject=True,
+                                                    dtype=torch.float32)
+            self.inference_model = self.ds_engine.module.eval()
+        else:
+            self.inference_model = self.inference_model.eval()
+			
         self.gpt.wte = self.mel_embedding
 
     def build_aligned_inputs_and_targets(self, input, start_token, stop_token):

tortoise/read.py

@@ -17,6 +17,7 @@ if __name__ == '__main__':
                                                  'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat')
     parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
     parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
+    parser.add_argument('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=True)
     parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
     parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice. Only the first candidate is actually used in the final product, the others can be used manually.', default=1)
     parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
@@ -25,7 +26,7 @@ if __name__ == '__main__':
     parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default=True)
 
     args = parser.parse_args()
-    tts = TextToSpeech(models_dir=args.model_dir)
+    tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed)
 
     outpath = args.output_path
     selected_voices = args.voice.split(',')