add flow decoder cache

cosyvoice/bin/export_jit.py

@@ -24,6 +24,7 @@ ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
 sys.path.append('{}/../..'.format(ROOT_DIR))
 sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
 from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
+from cosyvoice.utils.file_utils import logging
 
 
 def get_args():
@@ -71,6 +72,7 @@ def main():
         script.save('{}/llm.text_encoder.fp32.zip'.format(args.model_dir))
         script = get_optimized_script(llm_text_encoder.half())
         script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export llm_text_encoder')
 
         # 2. export llm llm
         llm_llm = model.model.llm.llm
@@ -78,14 +80,23 @@ def main():
         script.save('{}/llm.llm.fp32.zip'.format(args.model_dir))
         script = get_optimized_script(llm_llm.half(), ['forward_chunk'])
         script.save('{}/llm.llm.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export llm_llm')
 
-    # 3. export flow encoder
-    flow_encoder = model.model.flow.encoder
-    script = get_optimized_script(flow_encoder)
-    script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
-    script = get_optimized_script(flow_encoder.half())
-    script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
-
+        # 3. export flow encoder
+        flow_encoder = model.model.flow.encoder
+        script = get_optimized_script(flow_encoder)
+        script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
+        script = get_optimized_script(flow_encoder.half())
+        script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export flow_encoder')
+    else:
+        # 3. export flow encoder
+        flow_encoder = model.model.flow.encoder
+        script = get_optimized_script(flow_encoder, ['forward_chunk'])
+        script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
+        script = get_optimized_script(flow_encoder.half(), ['forward_chunk'])
+        script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export flow_encoder')
 
 if __name__ == '__main__':
     main()

cosyvoice/bin/export_onnx.py

@@ -28,6 +28,7 @@ ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
 sys.path.append('{}/../..'.format(ROOT_DIR))
 sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
 from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
+from cosyvoice.utils.file_utils import logging
 
 
 def get_dummy_input(batch_size, seq_len, out_channels, device):
@@ -51,6 +52,7 @@ def get_args():
     return args
 
 
+@torch.no_grad()
 def main():
     args = get_args()
     logging.basicConfig(level=logging.DEBUG,
@@ -64,52 +66,125 @@ def main():
         except Exception:
             raise TypeError('no valid model_type!')
 
-    # 1. export flow decoder estimator
-    estimator = model.model.flow.decoder.estimator
+    if not isinstance(model, CosyVoice2):
+        # 1. export flow decoder estimator
+        estimator = model.model.flow.decoder.estimator
+        estimator.eval()
 
-    device = model.model.device
-    batch_size, seq_len = 2, 256
-    out_channels = model.model.flow.decoder.estimator.out_channels
-    x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device)
-    torch.onnx.export(
-        estimator,
-        (x, mask, mu, t, spks, cond),
-        '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
-        export_params=True,
-        opset_version=18,
-        do_constant_folding=True,
-        input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
-        output_names=['estimator_out'],
-        dynamic_axes={
-            'x': {2: 'seq_len'},
-            'mask': {2: 'seq_len'},
-            'mu': {2: 'seq_len'},
-            'cond': {2: 'seq_len'},
-            'estimator_out': {2: 'seq_len'},
-        }
-    )
+        device = model.model.device
+        batch_size, seq_len = 2, 256
+        out_channels = model.model.flow.decoder.estimator.out_channels
+        x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device)
+        torch.onnx.export(
+            estimator,
+            (x, mask, mu, t, spks, cond),
+            '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+            export_params=True,
+            opset_version=18,
+            do_constant_folding=True,
+            input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
+            output_names=['estimator_out'],
+            dynamic_axes={
+                'x': {2: 'seq_len'},
+                'mask': {2: 'seq_len'},
+                'mu': {2: 'seq_len'},
+                'cond': {2: 'seq_len'},
+                'estimator_out': {2: 'seq_len'},
+            }
+        )
 
-    # 2. test computation consistency
-    option = onnxruntime.SessionOptions()
-    option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
-    option.intra_op_num_threads = 1
-    providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
-    estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
-                                                  sess_options=option, providers=providers)
+        # 2. test computation consistency
+        option = onnxruntime.SessionOptions()
+        option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+        option.intra_op_num_threads = 1
+        providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
+        estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+                                                    sess_options=option, providers=providers)
 
-    for _ in tqdm(range(10)):
-        x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)
-        output_pytorch = estimator(x, mask, mu, t, spks, cond)
-        ort_inputs = {
-            'x': x.cpu().numpy(),
-            'mask': mask.cpu().numpy(),
-            'mu': mu.cpu().numpy(),
-            't': t.cpu().numpy(),
-            'spks': spks.cpu().numpy(),
-            'cond': cond.cpu().numpy()
-        }
-        output_onnx = estimator_onnx.run(None, ort_inputs)[0]
-        torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4)
+        for _ in tqdm(range(10)):
+            x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)
+            output_pytorch = estimator(x, mask, mu, t, spks, cond)
+            ort_inputs = {
+                'x': x.cpu().numpy(),
+                'mask': mask.cpu().numpy(),
+                'mu': mu.cpu().numpy(),
+                't': t.cpu().numpy(),
+                'spks': spks.cpu().numpy(),
+                'cond': cond.cpu().numpy()
+            }
+            output_onnx = estimator_onnx.run(None, ort_inputs)[0]
+            torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4)
+        logging.info('successfully export estimator')
+    else:
+        # 1. export flow decoder estimator
+        estimator = model.model.flow.decoder.estimator
+        estimator.forward = estimator.forward_chunk
+        estimator.eval()
+
+        device = model.model.device
+        batch_size, seq_len = 2, 256
+        out_channels = model.model.flow.decoder.estimator.out_channels
+        x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device)
+        cache = model.model.init_flow_cache()['decoder_cache']
+        cache.pop('offset')
+        cache = {k: v[0] for k, v in cache.items()}
+        torch.onnx.export(
+            estimator,
+            (x, mask, mu, t, spks, cond,
+            cache['down_blocks_conv_cache'],
+            cache['down_blocks_kv_cache'],
+            cache['mid_blocks_conv_cache'],
+            cache['mid_blocks_kv_cache'],
+            cache['up_blocks_conv_cache'],
+            cache['up_blocks_kv_cache'],
+            cache['final_blocks_conv_cache']),
+            '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+            export_params=True,
+            opset_version=18,
+            do_constant_folding=True,
+            input_names=['x', 'mask', 'mu', 't', 'spks', 'cond', 'down_blocks_conv_cache', 'down_blocks_kv_cache', 'mid_blocks_conv_cache', 'mid_blocks_kv_cache', 'up_blocks_conv_cache', 'up_blocks_kv_cache', 'final_blocks_conv_cache'],
+            output_names=['estimator_out', 'down_blocks_conv_cache_out', 'down_blocks_kv_cache_out', 'mid_blocks_conv_cache_out', 'mid_blocks_kv_cache_out', 'up_blocks_conv_cache_out', 'up_blocks_kv_cache_out', 'final_blocks_conv_cache_out'],
+            dynamic_axes={
+                'x': {2: 'seq_len'},
+                'mask': {2: 'seq_len'},
+                'mu': {2: 'seq_len'},
+                'cond': {2: 'seq_len'},
+                'down_blocks_kv_cache': {3: 'seq_len'},
+                'mid_blocks_kv_cache': {3: 'seq_len'},
+                'up_blocks_kv_cache': {3: 'seq_len'},
+                'estimator_out': {2: 'seq_len'},
+                'down_blocks_kv_cache_out': {3: 'seq_len'},
+                'mid_blocks_kv_cache_out': {3: 'seq_len'},
+                'up_blocks_kv_cache_out': {3: 'seq_len'},
+            }
+        )
+
+        # 2. test computation consistency
+        option = onnxruntime.SessionOptions()
+        option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+        option.intra_op_num_threads = 1
+        providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
+        estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+                                                    sess_options=option, providers=providers)
+
+        for _ in tqdm(range(10)):
+            x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)
+            cache = model.model.init_flow_cache()['decoder_cache']
+            cache.pop('offset')
+            cache = {k: v[0] for k, v in cache.items()}
+            output_pytorch = estimator(x, mask, mu, t, spks, cond, **{k: v.clone() for k, v in cache.items()})
+            ort_inputs = {
+                'x': x.cpu().numpy(),
+                'mask': mask.cpu().numpy(),
+                'mu': mu.cpu().numpy(),
+                't': t.cpu().numpy(),
+                'spks': spks.cpu().numpy(),
+                'cond': cond.cpu().numpy(),
+            }
+            output_onnx = estimator_onnx.run(None, {**ort_inputs, **{k: v.clone().cpu().numpy() for k, v in cache.items()}})
+            for i, j in zip(output_pytorch, output_onnx):
+                torch.testing.assert_allclose(i, torch.from_numpy(j).to(device), rtol=1e-2, atol=1e-4)
+        logging.info('successfully export estimator')
 
 
 if __name__ == "__main__":

cosyvoice/bin/export_trt.sh

@@ -3,8 +3,23 @@
 # download tensorrt from https://developer.nvidia.com/tensorrt/download/10x, check your system and cuda for compatibability
 # for example for linux + cuda12.4, you can download https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.1/tars/TensorRT-10.0.1.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
 TRT_DIR=<YOUR_TRT_DIR>
-MODEL_DIR=<COSYVOICE2_MODEL_DIR>
-
+MODEL_DIR=<YOUR_MODEL_DIR>
 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TRT_DIR/lib:/usr/local/cuda/lib64
+
+# cosyvoice export
 $TRT_DIR/bin/trtexec --onnx=$MODEL_DIR/flow.decoder.estimator.fp32.onnx --saveEngine=$MODEL_DIR/flow.decoder.estimator.fp32.mygpu.plan --minShapes=x:2x80x4,mask:2x1x4,mu:2x80x4,cond:2x80x4 --optShapes=x:2x80x193,mask:2x1x193,mu:2x80x193,cond:2x80x193 --maxShapes=x:2x80x6800,mask:2x1x6800,mu:2x80x6800,cond:2x80x6800 --inputIOFormats=fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw --outputIOFormats=fp32:chw
 $TRT_DIR/bin/trtexec --onnx=$MODEL_DIR/flow.decoder.estimator.fp32.onnx --saveEngine=$MODEL_DIR/flow.decoder.estimator.fp16.mygpu.plan --fp16 --minShapes=x:2x80x4,mask:2x1x4,mu:2x80x4,cond:2x80x4 --optShapes=x:2x80x193,mask:2x1x193,mu:2x80x193,cond:2x80x193 --maxShapes=x:2x80x6800,mask:2x1x6800,mu:2x80x6800,cond:2x80x6800 --inputIOFormats=fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw --outputIOFormats=fp16:chw
+
+# cosyvoice2 export with cache
+$TRT_DIR/bin/trtexec --onnx=$MODEL_DIR/flow.decoder.estimator.fp32.onnx --saveEngine=$MODEL_DIR/flow.decoder.estimator.fp32.mygpu.plan \
+    --minShapes=x:2x80x4,mask:2x1x4,mu:2x80x4,cond:2x80x4,down_blocks_kv_cache:1x4x2x0x512x2,mid_blocks_kv_cache:12x4x2x0x512x2,up_blocks_kv_cache:1x4x2x0x512x2 \
+    --optShapes=x:2x80x193,mask:2x1x193,mu:2x80x193,cond:2x80x193,down_blocks_kv_cache:1x4x2x193x512x2,mid_blocks_kv_cache:12x4x2x193x512x2,up_blocks_kv_cache:1x4x2x193x512x2 \
+    --maxShapes=x:2x80x6800,mask:2x1x6800,mu:2x80x6800,cond:2x80x6800,down_blocks_kv_cache:1x4x2x200x512x2,mid_blocks_kv_cache:12x4x2x200x512x2,up_blocks_kv_cache:1x4x2x200x512x2 \
+    --inputIOFormats=fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw \
+    --outputIOFormats=fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw,fp32:chw
+$TRT_DIR/bin/trtexec --onnx=$MODEL_DIR/flow.decoder.estimator.fp32.onnx --saveEngine=$MODEL_DIR/flow.decoder.estimator.fp16.mygpu.plan --fp16 \
+    --minShapes=x:2x80x4,mask:2x1x4,mu:2x80x4,cond:2x80x4,down_blocks_kv_cache:1x4x2x0x512x2,mid_blocks_kv_cache:12x4x2x0x512x2,up_blocks_kv_cache:1x4x2x0x512x2 \
+    --optShapes=x:2x80x193,mask:2x1x193,mu:2x80x193,cond:2x80x193,down_blocks_kv_cache:1x4x2x193x512x2,mid_blocks_kv_cache:12x4x2x193x512x2,up_blocks_kv_cache:1x4x2x193x512x2 \
+    --maxShapes=x:2x80x6800,mask:2x1x6800,mu:2x80x6800,cond:2x80x6800,down_blocks_kv_cache:1x4x2x200x512x2,mid_blocks_kv_cache:12x4x2x200x512x2,up_blocks_kv_cache:1x4x2x200x512x2 \
+    --inputIOFormats=fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw \
+    --outputIOFormats=fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw,fp16:chw

cosyvoice/bin/inference.py

@@ -23,7 +23,7 @@ from torch.utils.data import DataLoader
 import torchaudio
 from hyperpyyaml import load_hyperpyyaml
 from tqdm import tqdm
-from cosyvoice.cli.model import CosyVoiceModel
+from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
 from cosyvoice.dataset.dataset import Dataset
 
 
@@ -33,6 +33,7 @@ def get_args():
     parser.add_argument('--prompt_data', required=True, help='prompt data file')
     parser.add_argument('--prompt_utt2data', required=True, help='prompt data file')
     parser.add_argument('--tts_text', required=True, help='tts input file')
+    parser.add_argument('--qwen_pretrain_path', required=False, help='qwen pretrain path')
     parser.add_argument('--llm_model', required=True, help='llm model file')
     parser.add_argument('--flow_model', required=True, help='flow model file')
     parser.add_argument('--hifigan_model', required=True, help='hifigan model file')
@@ -59,10 +60,18 @@ def main():
     # Init cosyvoice models from configs
     use_cuda = args.gpu >= 0 and torch.cuda.is_available()
     device = torch.device('cuda' if use_cuda else 'cpu')
-    with open(args.config, 'r') as f:
-        configs = load_hyperpyyaml(f)
+    try:
+        with open(args.config, 'r') as f:
+            configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': args.qwen_pretrain_path})
+        model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16=False)
+    except Exception:
+        try:
+            with open(args.config, 'r') as f:
+                configs = load_hyperpyyaml(f)
+            model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16=False)
+        except Exception:
+            raise TypeError('no valid model_type!')
 
-    model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
     model.load(args.llm_model, args.flow_model, args.hifigan_model)
 
     test_dataset = Dataset(args.prompt_data, data_pipeline=configs['data_pipeline'], mode='inference', shuffle=False, partition=False,
@@ -104,7 +113,7 @@ def main():
             tts_speeches = torch.concat(tts_speeches, dim=1)
             tts_key = '{}_{}'.format(utts[0], tts_index[0])
             tts_fn = os.path.join(args.result_dir, '{}.wav'.format(tts_key))
-            torchaudio.save(tts_fn, tts_speeches, sample_rate=22050)
+            torchaudio.save(tts_fn, tts_speeches, sample_rate=configs['sample_rate'], backend='soundfile')
             f.write('{} {}\n'.format(tts_key, tts_fn))
             f.flush()
     f.close()

cosyvoice/bin/train.py

@@ -46,6 +46,7 @@ def get_args():
     parser.add_argument('--config', required=True, help='config file')
     parser.add_argument('--train_data', required=True, help='train data file')
     parser.add_argument('--cv_data', required=True, help='cv data file')
+    parser.add_argument('--qwen_pretrain_path', required=False, help='qwen pretrain path')
     parser.add_argument('--checkpoint', help='checkpoint model')
     parser.add_argument('--model_dir', required=True, help='save model dir')
     parser.add_argument('--tensorboard_dir',
@@ -97,8 +98,12 @@ def main():
     override_dict = {k: None for k in ['llm', 'flow', 'hift', 'hifigan'] if k != args.model}
     if gan is True:
         override_dict.pop('hift')
-    with open(args.config, 'r') as f:
-        configs = load_hyperpyyaml(f, overrides=override_dict)
+    try:
+        with open(args.config, 'r') as f:
+            configs = load_hyperpyyaml(f, overrides={**override_dict, 'qwen_pretrain_path': args.qwen_pretrain_path})
+    except Exception:
+        with open(args.config, 'r') as f:
+            configs = load_hyperpyyaml(f, overrides=override_dict)
     if gan is True:
         configs['train_conf'] = configs['train_conf_gan']
     configs['train_conf'].update(vars(args))

cosyvoice/cli/cosyvoice.py

@@ -32,7 +32,10 @@ class CosyVoice:
         self.fp16 = fp16
         if not os.path.exists(model_dir):
             model_dir = snapshot_download(model_dir)
-        with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
+        hyper_yaml_path = '{}/cosyvoice.yaml'.format(model_dir)
+        if not os.path.exists(hyper_yaml_path):
+            raise ValueError('{} not found!'.format(hyper_yaml_path))
+        with open(hyper_yaml_path, 'r') as f:
             configs = load_hyperpyyaml(f)
         assert get_model_type(configs) != CosyVoice2Model, 'do not use {} for CosyVoice initialization!'.format(model_dir)
         self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
@@ -132,7 +135,10 @@ class CosyVoice2(CosyVoice):
         self.fp16 = fp16
         if not os.path.exists(model_dir):
             model_dir = snapshot_download(model_dir)
-        with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
+        hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir)
+        if not os.path.exists(hyper_yaml_path):
+            raise ValueError('{} not found!'.format(hyper_yaml_path))
+        with open(hyper_yaml_path, 'r') as f:
             configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
         assert get_model_type(configs) == CosyVoice2Model, 'do not use {} for CosyVoice2 initialization!'.format(model_dir)
         self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],

cosyvoice/cli/model.py

@@ -44,8 +44,6 @@ class CosyVoiceModel:
         self.token_min_hop_len = 2 * self.flow.input_frame_rate
         self.token_max_hop_len = 4 * self.flow.input_frame_rate
         self.token_overlap_len = 20
-        # here we fix set flow.decoder.estimator.static_chunk_size = 0 for compatibability
-        self.flow.decoder.estimator.static_chunk_size = 0
         # mel fade in out
         self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
         self.mel_window = np.hamming(2 * self.mel_overlap_len)
@@ -121,15 +119,14 @@ class CosyVoiceModel:
         self.llm_end_dict[uuid] = True
 
     def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
-        tts_mel, flow_cache = self.flow.inference(token=token.to(self.device),
-                                                  token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
-                                                  prompt_token=prompt_token.to(self.device),
-                                                  prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
-                                                  prompt_feat=prompt_feat.to(self.device),
-                                                  prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
-                                                  embedding=embedding.to(self.device),
-                                                  flow_cache=self.flow_cache_dict[uuid])
-        self.flow_cache_dict[uuid] = flow_cache
+        tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device),
+                                                                  token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+                                                                  prompt_token=prompt_token.to(self.device),
+                                                                  prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+                                                                  prompt_feat=prompt_feat.to(self.device),
+                                                                  prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+                                                                  embedding=embedding.to(self.device),
+                                                                  flow_cache=self.flow_cache_dict[uuid])
 
         # mel overlap fade in out
         if self.mel_overlap_dict[uuid].shape[2] != 0:
@@ -276,6 +273,7 @@ class CosyVoiceModel:
             self.llm_end_dict.pop(this_uuid)
             self.mel_overlap_dict.pop(this_uuid)
             self.hift_cache_dict.pop(this_uuid)
+            self.flow_cache_dict.pop(this_uuid)
         torch.cuda.empty_cache()
 
 
@@ -297,9 +295,8 @@ class CosyVoice2Model(CosyVoiceModel):
             self.llm.half()
             self.flow.half()
         self.token_hop_len = 2 * self.flow.input_frame_rate
-        # here we fix flow encoder/decoder decoding_chunk_size, in the future we will send it as arguments, or use cache
-        self.flow.encoder.static_chunk_size = 2 * self.flow.input_frame_rate
-        self.flow.decoder.estimator.static_chunk_size = 2 * self.flow.input_frame_rate * self.flow.token_mel_ratio
+        # flow decoder required_cache_size
+        self.flow_decoder_required_cache_size = self.flow.decoder.estimator.num_decoding_left_chunks * self.flow.input_frame_rate * self.flow.token_mel_ratio
         # hift cache
         self.mel_cache_len = 8
         self.source_cache_len = int(self.mel_cache_len * 480)
@@ -312,22 +309,49 @@ class CosyVoice2Model(CosyVoiceModel):
         # dict used to store session related variable
         self.tts_speech_token_dict = {}
         self.llm_end_dict = {}
+        self.flow_cache_dict = {}
         self.hift_cache_dict = {}
 
+    def init_flow_cache(self):
+        encoder_cache = {'offset': 0,
+                         'pre_lookahead_layer_conv2_cache': torch.zeros(1, 512, 2).to(self.device),
+                         'encoders_kv_cache': torch.zeros(6, 1, 8, 0, 64 * 2).to(self.device),
+                         'upsample_offset': 0,
+                         'upsample_conv_cache': torch.zeros(1, 512, 4).to(self.device),
+                         'upsample_kv_cache': torch.zeros(4, 1, 8, 0, 64 * 2).to(self.device)}
+        decoder_cache = {'offset': 0,
+                         'down_blocks_conv_cache': torch.zeros(10, 1, 2, 832, 2).to(self.device),
+                         'down_blocks_kv_cache': torch.zeros(10, 1, 4, 2, 0, 512, 2).to(self.device),
+                         'mid_blocks_conv_cache': torch.zeros(10, 12, 2, 512, 2).to(self.device),
+                         'mid_blocks_kv_cache': torch.zeros(10, 12, 4, 2, 0, 512, 2).to(self.device),
+                         'up_blocks_conv_cache': torch.zeros(10, 1, 2, 1024, 2).to(self.device),
+                         'up_blocks_kv_cache': torch.zeros(10, 1, 4, 2, 0, 512, 2).to(self.device),
+                         'final_blocks_conv_cache': torch.zeros(10, 2, 256, 2).to(self.device)}
+        cache = {'encoder_cache': encoder_cache, 'decoder_cache': decoder_cache}
+        return cache
+
+    def trim_flow_cache(self, cache):
+        if cache['decoder_cache']['down_blocks_kv_cache'].size(4) > self.flow_decoder_required_cache_size:
+            cache['decoder_cache']['down_blocks_kv_cache'] = cache['decoder_cache']['down_blocks_kv_cache'][:, :, :, :, -self.flow_decoder_required_cache_size:]
+            cache['decoder_cache']['mid_blocks_kv_cache'] = cache['decoder_cache']['mid_blocks_kv_cache'][:, :, :, :, -self.flow_decoder_required_cache_size:]
+            cache['decoder_cache']['up_blocks_kv_cache'] = cache['decoder_cache']['up_blocks_kv_cache'][:, :, :, :, -self.flow_decoder_required_cache_size:]
+        return cache
+
     def load_jit(self, flow_encoder_model):
         flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
         self.flow.encoder = flow_encoder
 
-    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0):
-        tts_mel, _ = self.flow.inference(token=token.to(self.device),
-                                         token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
-                                         prompt_token=prompt_token.to(self.device),
-                                         prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
-                                         prompt_feat=prompt_feat.to(self.device),
-                                         prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
-                                         embedding=embedding.to(self.device),
-                                         finalize=finalize)
-        tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
+    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
+        tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device),
+                                                                  token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+                                                                  prompt_token=prompt_token.to(self.device),
+                                                                  prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+                                                                  prompt_feat=prompt_feat.to(self.device),
+                                                                  prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+                                                                  embedding=embedding.to(self.device),
+                                                                  cache=self.flow_cache_dict[uuid],
+                                                                  finalize=finalize)
+        self.flow_cache_dict[uuid] = self.trim_flow_cache(self.flow_cache_dict[uuid])
         # append hift cache
         if self.hift_cache_dict[uuid] is not None:
             hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
@@ -362,24 +386,27 @@ class CosyVoice2Model(CosyVoiceModel):
         with self.lock:
             self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
             self.hift_cache_dict[this_uuid] = None
+            self.flow_cache_dict[this_uuid] = self.init_flow_cache()
         p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
         p.start()
         if stream is True:
-            token_offset = 0
             while True:
                 time.sleep(0.1)
-                if len(self.tts_speech_token_dict[this_uuid]) - token_offset >= self.token_hop_len + self.flow.pre_lookahead_len:
-                    this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_offset + self.token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0)
+                if len(self.tts_speech_token_dict[this_uuid]) >= self.token_hop_len + self.flow.pre_lookahead_len:
+                    this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:self.token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0)
                     this_tts_speech = self.token2wav(token=this_tts_speech_token,
                                                      prompt_token=flow_prompt_speech_token,
                                                      prompt_feat=prompt_speech_feat,
                                                      embedding=flow_embedding,
                                                      uuid=this_uuid,
-                                                     token_offset=token_offset,
                                                      finalize=False)
-                    token_offset += self.token_hop_len
+                    # NOTE in cache inference mode, we only use flow_prompt_speech_token/prompt_speech_feat in first chunk
+                    flow_prompt_speech_token = torch.zeros(1, 0, dtype=torch.int32).to(self.device)
+                    prompt_speech_feat = torch.zeros(1, 0, 80).to(self.device)
                     yield {'tts_speech': this_tts_speech.cpu()}
-                if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) - token_offset < self.token_hop_len + self.flow.pre_lookahead_len:
+                    with self.lock:
+                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][self.token_hop_len:]
+                if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < self.token_hop_len + self.flow.pre_lookahead_len:
                     break
             p.join()
             # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
@@ -389,7 +416,6 @@ class CosyVoice2Model(CosyVoiceModel):
                                              prompt_feat=prompt_speech_feat,
                                              embedding=flow_embedding,
                                              uuid=this_uuid,
-                                             token_offset=token_offset,
                                              finalize=True)
             yield {'tts_speech': this_tts_speech.cpu()}
         else:
@@ -401,11 +427,12 @@ class CosyVoice2Model(CosyVoiceModel):
                                              prompt_feat=prompt_speech_feat,
                                              embedding=flow_embedding,
                                              uuid=this_uuid,
-                                             token_offset=0,
                                              finalize=True,
                                              speed=speed)
             yield {'tts_speech': this_tts_speech.cpu()}
         with self.lock:
             self.tts_speech_token_dict.pop(this_uuid)
             self.llm_end_dict.pop(this_uuid)
+            self.hift_cache_dict.pop(this_uuid)
+            self.flow_cache_dict.pop(this_uuid)
         torch.cuda.empty_cache()

cosyvoice/flow/decoder.py

@@ -11,14 +11,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Tuple, Optional, Dict, Any
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import pack, rearrange, repeat
+from diffusers.models.attention_processor import Attention, AttnProcessor2_0, inspect, logger, deprecate
 from cosyvoice.utils.common import mask_to_bias
 from cosyvoice.utils.mask import add_optional_chunk_mask
 from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
-from matcha.models.components.transformer import BasicTransformerBlock
+from matcha.models.components.transformer import BasicTransformerBlock, maybe_allow_in_graph
 
 
 class Transpose(torch.nn.Module):
@@ -27,34 +29,11 @@ class Transpose(torch.nn.Module):
         self.dim0 = dim0
         self.dim1 = dim1
 
-    def forward(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
         x = torch.transpose(x, self.dim0, self.dim1)
         return x
 
 
-class CausalBlock1D(Block1D):
-    def __init__(self, dim: int, dim_out: int):
-        super(CausalBlock1D, self).__init__(dim, dim_out)
-        self.block = torch.nn.Sequential(
-            CausalConv1d(dim, dim_out, 3),
-            Transpose(1, 2),
-            nn.LayerNorm(dim_out),
-            Transpose(1, 2),
-            nn.Mish(),
-        )
-
-    def forward(self, x: torch.Tensor, mask: torch.Tensor):
-        output = self.block(x * mask)
-        return output * mask
-
-
-class CausalResnetBlock1D(ResnetBlock1D):
-    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
-        super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
-        self.block1 = CausalBlock1D(dim, dim_out)
-        self.block2 = CausalBlock1D(dim_out, dim_out)
-
-
 class CausalConv1d(torch.nn.Conv1d):
     def __init__(
         self,
@@ -76,12 +55,332 @@ class CausalConv1d(torch.nn.Conv1d):
                                            padding_mode=padding_mode,
                                            device=device, dtype=dtype)
         assert stride == 1
-        self.causal_padding = (kernel_size - 1, 0)
+        self.causal_padding = kernel_size - 1
 
-    def forward(self, x: torch.Tensor):
-        x = F.pad(x, self.causal_padding)
+    def forward(self, x: torch.Tensor, cache: torch.Tensor=torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
+        if cache.size(2) == 0:
+            x = F.pad(x, (self.causal_padding, 0), value=0.0)
+        else:
+            assert cache.size(2) == self.causal_padding
+            x = torch.concat([cache, x], dim=2)
+        cache = x[:, :, -self.causal_padding:]
         x = super(CausalConv1d, self).forward(x)
-        return x
+        return x, cache
+
+
+class CausalBlock1D(Block1D):
+    def __init__(self, dim: int, dim_out: int):
+        super(CausalBlock1D, self).__init__(dim, dim_out)
+        self.block = torch.nn.Sequential(
+            CausalConv1d(dim, dim_out, 3),
+            Transpose(1, 2),
+            nn.LayerNorm(dim_out),
+            Transpose(1, 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: torch.Tensor, mask: torch.Tensor, cache: torch.Tensor=torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
+        output, cache = self.block[0](x * mask, cache)
+        for i in range(1, len(self.block)):
+            output = self.block[i](output)
+        return output * mask, cache
+
+
+class CausalResnetBlock1D(ResnetBlock1D):
+    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
+        super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
+        self.block1 = CausalBlock1D(dim, dim_out)
+        self.block2 = CausalBlock1D(dim_out, dim_out)
+
+    def forward(self, x: torch.Tensor, mask: torch.Tensor, time_emb: torch.Tensor, block1_cache: torch.Tensor=torch.zeros(0, 0, 0), block2_cache: torch.Tensor=torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        h, block1_cache = self.block1(x, mask, block1_cache)
+        h += self.mlp(time_emb).unsqueeze(-1)
+        h, block2_cache = self.block2(h, mask, block2_cache)
+        output = h + self.res_conv(x * mask)
+        return output, block1_cache, block2_cache
+
+
+class CausalAttnProcessor2_0(AttnProcessor2_0):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        super(CausalAttnProcessor2_0, self).__init__()
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        *args,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, torch.Tensor]:
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            deprecate("scale", "1.0.0", deprecation_message)
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            # NOTE do not use attn.prepare_attention_mask as we have already provided the correct attention_mask
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.unsqueeze(dim=1).repeat(1, attn.heads, 1, 1)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key_cache = attn.to_k(encoder_hidden_states)
+        value_cache = attn.to_v(encoder_hidden_states)
+        # NOTE here we judge cache.size(0) instead of cache.size(1), because init_cache has size (2, 0, 512, 2)
+        if cache.size(0) != 0:
+            key = torch.concat([cache[:, :, :, 0], key_cache], dim=1)
+            value = torch.concat([cache[:, :, :, 1], value_cache], dim=1)
+        else:
+            key, value = key_cache, value_cache
+        cache = torch.stack([key_cache, value_cache], dim=3)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states, cache
+
+
+@maybe_allow_in_graph
+class CausalAttention(Attention):
+    def __init__(
+        self,
+        query_dim: int,
+        cross_attention_dim: Optional[int] = None,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        bias: bool = False,
+        upcast_attention: bool = False,
+        upcast_softmax: bool = False,
+        cross_attention_norm: Optional[str] = None,
+        cross_attention_norm_num_groups: int = 32,
+        added_kv_proj_dim: Optional[int] = None,
+        norm_num_groups: Optional[int] = None,
+        spatial_norm_dim: Optional[int] = None,
+        out_bias: bool = True,
+        scale_qk: bool = True,
+        only_cross_attention: bool = False,
+        eps: float = 1e-5,
+        rescale_output_factor: float = 1.0,
+        residual_connection: bool = False,
+        _from_deprecated_attn_block: bool = False,
+        processor: Optional["AttnProcessor2_0"] = None,
+        out_dim: int = None,
+    ):
+        super(CausalAttention, self).__init__(query_dim, cross_attention_dim, heads, dim_head, dropout, bias, upcast_attention, upcast_softmax, cross_attention_norm, cross_attention_norm_num_groups,
+                                              added_kv_proj_dim, norm_num_groups, spatial_norm_dim, out_bias, scale_qk, only_cross_attention, eps, rescale_output_factor, residual_connection, _from_deprecated_attn_block, processor, out_dim)
+        processor = CausalAttnProcessor2_0()
+        self.set_processor(processor)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        **cross_attention_kwargs,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""
+        The forward method of the `Attention` class.
+
+        Args:
+            hidden_states (`torch.Tensor`):
+                The hidden states of the query.
+            encoder_hidden_states (`torch.Tensor`, *optional*):
+                The hidden states of the encoder.
+            attention_mask (`torch.Tensor`, *optional*):
+                The attention mask to use. If `None`, no mask is applied.
+            **cross_attention_kwargs:
+                Additional keyword arguments to pass along to the cross attention.
+
+        Returns:
+            `torch.Tensor`: The output of the attention layer.
+        """
+        # The `Attention` class can call different attention processors / attention functions
+        # here we simply pass along all tensors to the selected processor class
+        # For standard processors that are defined here, `**cross_attention_kwargs` is empty
+
+        attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
+        unused_kwargs = [k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters]
+        if len(unused_kwargs) > 0:
+            logger.warning(
+                f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
+            )
+        cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}
+
+        return self.processor(
+            self,
+            hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            cache=cache,
+            **cross_attention_kwargs,
+        )
+
+
+@maybe_allow_in_graph
+class CausalBasicTransformerBlock(BasicTransformerBlock):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",
+        final_dropout: bool = False,
+    ):
+        super(CausalBasicTransformerBlock, self).__init__(dim, num_attention_heads, attention_head_dim, dropout, cross_attention_dim, activation_fn, num_embeds_ada_norm,
+                                                          attention_bias, only_cross_attention, double_self_attention, upcast_attention, norm_elementwise_affine, norm_type, final_dropout)
+        self.attn1 = CausalAttention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 1. Self-Attention
+        if self.use_ada_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.use_ada_layer_norm_zero:
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        else:
+            norm_hidden_states = self.norm1(hidden_states)
+
+        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+
+        attn_output, cache = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask,
+            cache=cache,
+            **cross_attention_kwargs,
+        )
+        if self.use_ada_layer_norm_zero:
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = attn_output + hidden_states
+
+        # 2. Cross-Attention
+        if self.attn2 is not None:
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+            )
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+            hidden_states = attn_output + hidden_states
+
+        # 3. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states, cache
 
 
 class ConditionalDecoder(nn.Module):
@@ -89,7 +388,6 @@ class ConditionalDecoder(nn.Module):
         self,
         in_channels,
         out_channels,
-        causal=False,
         channels=(256, 256),
         dropout=0.05,
         attention_head_dim=64,
@@ -106,7 +404,7 @@ class ConditionalDecoder(nn.Module):
         channels = tuple(channels)
         self.in_channels = in_channels
         self.out_channels = out_channels
-        self.causal = causal
+
         self.time_embeddings = SinusoidalPosEmb(in_channels)
         time_embed_dim = channels[0] * 4
         self.time_mlp = TimestepEmbedding(
@@ -123,8 +421,7 @@ class ConditionalDecoder(nn.Module):
             input_channel = output_channel
             output_channel = channels[i]
             is_last = i == len(channels) - 1
-            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else \
-                ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
             transformer_blocks = nn.ModuleList(
                 [
                     BasicTransformerBlock(
@@ -138,16 +435,14 @@ class ConditionalDecoder(nn.Module):
                 ]
             )
             downsample = (
-                Downsample1D(output_channel) if not is_last else
-                CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+                Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
             )
             self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
 
         for _ in range(num_mid_blocks):
             input_channel = channels[-1]
             out_channels = channels[-1]
-            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else \
-                ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
 
             transformer_blocks = nn.ModuleList(
                 [
@@ -169,11 +464,7 @@ class ConditionalDecoder(nn.Module):
             input_channel = channels[i] * 2
             output_channel = channels[i + 1]
             is_last = i == len(channels) - 2
-            resnet = CausalResnetBlock1D(
-                dim=input_channel,
-                dim_out=output_channel,
-                time_emb_dim=time_embed_dim,
-            ) if self.causal else ResnetBlock1D(
+            resnet = ResnetBlock1D(
                 dim=input_channel,
                 dim_out=output_channel,
                 time_emb_dim=time_embed_dim,
@@ -193,10 +484,10 @@ class ConditionalDecoder(nn.Module):
             upsample = (
                 Upsample1D(output_channel, use_conv_transpose=True)
                 if not is_last
-                else CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+                else nn.Conv1d(output_channel, output_channel, 3, padding=1)
             )
             self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
-        self.final_block = CausalBlock1D(channels[-1], channels[-1]) if self.causal else Block1D(channels[-1], channels[-1])
+        self.final_block = Block1D(channels[-1], channels[-1])
         self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
         self.initialize_weights()
 
@@ -249,9 +540,8 @@ class ConditionalDecoder(nn.Module):
             mask_down = masks[-1]
             x = resnet(x, mask_down, t)
             x = rearrange(x, "b c t -> b t c").contiguous()
-            # attn_mask = torch.matmul(mask_down.transpose(1, 2).contiguous(), mask_down)
-            attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1)
-            attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
+            attn_mask = (torch.matmul(mask_down.transpose(1, 2).contiguous(), mask_down) == 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
             for transformer_block in transformer_blocks:
                 x = transformer_block(
                     hidden_states=x,
@@ -268,9 +558,8 @@ class ConditionalDecoder(nn.Module):
         for resnet, transformer_blocks in self.mid_blocks:
             x = resnet(x, mask_mid, t)
             x = rearrange(x, "b c t -> b t c").contiguous()
-            # attn_mask = torch.matmul(mask_mid.transpose(1, 2).contiguous(), mask_mid)
-            attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1)
-            attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
+            attn_mask = (torch.matmul(mask_mid.transpose(1, 2).contiguous(), mask_mid) == 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
             for transformer_block in transformer_blocks:
                 x = transformer_block(
                     hidden_states=x,
@@ -285,9 +574,8 @@ class ConditionalDecoder(nn.Module):
             x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
             x = resnet(x, mask_up, t)
             x = rearrange(x, "b c t -> b t c").contiguous()
-            # attn_mask = torch.matmul(mask_up.transpose(1, 2).contiguous(), mask_up)
-            attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1)
-            attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
+            attn_mask = (torch.matmul(mask_up.transpose(1, 2).contiguous(), mask_up) == 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
             for transformer_block in transformer_blocks:
                 x = transformer_block(
                     hidden_states=x,
@@ -299,3 +587,296 @@ class ConditionalDecoder(nn.Module):
         x = self.final_block(x, mask_up)
         output = self.final_proj(x * mask_up)
         return output * mask
+
+
+class CausalConditionalDecoder(ConditionalDecoder):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        channels=(256, 256),
+        dropout=0.05,
+        attention_head_dim=64,
+        n_blocks=1,
+        num_mid_blocks=2,
+        num_heads=4,
+        act_fn="snake",
+        static_chunk_size=50,
+        num_decoding_left_chunks=2,
+    ):
+        """
+        This decoder requires an input with the same shape of the target. So, if your text content
+        is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
+        """
+        torch.nn.Module.__init__(self)
+        channels = tuple(channels)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.time_embeddings = SinusoidalPosEmb(in_channels)
+        time_embed_dim = channels[0] * 4
+        self.time_mlp = TimestepEmbedding(
+            in_channels=in_channels,
+            time_embed_dim=time_embed_dim,
+            act_fn="silu",
+        )
+        self.static_chunk_size = static_chunk_size
+        self.num_decoding_left_chunks = num_decoding_left_chunks
+        self.down_blocks = nn.ModuleList([])
+        self.mid_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        output_channel = in_channels
+        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
+            input_channel = output_channel
+            output_channel = channels[i]
+            is_last = i == len(channels) - 1
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            transformer_blocks = nn.ModuleList(
+                [
+                    CausalBasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            downsample = (
+                Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3)
+            )
+            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+        for _ in range(num_mid_blocks):
+            input_channel = channels[-1]
+            out_channels = channels[-1]
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+            transformer_blocks = nn.ModuleList(
+                [
+                    CausalBasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+
+            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+        channels = channels[::-1] + (channels[0],)
+        for i in range(len(channels) - 1):
+            input_channel = channels[i] * 2
+            output_channel = channels[i + 1]
+            is_last = i == len(channels) - 2
+            resnet = CausalResnetBlock1D(
+                dim=input_channel,
+                dim_out=output_channel,
+                time_emb_dim=time_embed_dim,
+            )
+            transformer_blocks = nn.ModuleList(
+                [
+                    CausalBasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            upsample = (
+                Upsample1D(output_channel, use_conv_transpose=True)
+                if not is_last
+                else CausalConv1d(output_channel, output_channel, 3)
+            )
+            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+        self.final_block = CausalBlock1D(channels[-1], channels[-1])
+        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+        self.initialize_weights()
+
+    def forward(self, x, mask, mu, t, spks=None, cond=None):
+        """Forward pass of the UNet1DConditional model.
+
+        Args:
+            x (torch.Tensor): shape (batch_size, in_channels, time)
+            mask (_type_): shape (batch_size, 1, time)
+            t (_type_): shape (batch_size)
+            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+            cond (_type_, optional): placeholder for future use. Defaults to None.
+
+        Raises:
+            ValueError: _description_
+            ValueError: _description_
+
+        Returns:
+            _type_: _description_
+        """
+
+        t = self.time_embeddings(t).to(t.dtype)
+        t = self.time_mlp(t)
+
+        x = pack([x, mu], "b * t")[0]
+
+        if spks is not None:
+            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+            x = pack([x, spks], "b * t")[0]
+        if cond is not None:
+            x = pack([x, cond], "b * t")[0]
+
+        hiddens = []
+        masks = [mask]
+        for resnet, transformer_blocks, downsample in self.down_blocks:
+            mask_down = masks[-1]
+            x, _, _ = resnet(x, mask_down, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, self.num_decoding_left_chunks)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x, _ = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            hiddens.append(x)  # Save hidden states for skip connections
+            x, _ = downsample(x * mask_down)
+            masks.append(mask_down[:, :, ::2])
+        masks = masks[:-1]
+        mask_mid = masks[-1]
+
+        for resnet, transformer_blocks in self.mid_blocks:
+            x, _, _ = resnet(x, mask_mid, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, self.num_decoding_left_chunks)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x, _ = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+
+        for resnet, transformer_blocks, upsample in self.up_blocks:
+            mask_up = masks.pop()
+            skip = hiddens.pop()
+            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+            x, _, _ = resnet(x, mask_up, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, self.num_decoding_left_chunks)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x, _ = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            x, _ = upsample(x * mask_up)
+        x, _ = self.final_block(x, mask_up)
+        output = self.final_proj(x * mask_up)
+        return output * mask
+
+    def forward_chunk(self, x, mask, mu, t, spks=None, cond=None,
+        down_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        down_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0),
+        mid_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        mid_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0),
+        up_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        up_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0),
+        final_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0)
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Forward pass of the UNet1DConditional model.
+
+        Args:
+            x (torch.Tensor): shape (batch_size, in_channels, time)
+            mask (_type_): shape (batch_size, 1, time)
+            t (_type_): shape (batch_size)
+            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+            cond (_type_, optional): placeholder for future use. Defaults to None.
+
+        Raises:
+            ValueError: _description_
+            ValueError: _description_
+
+        Returns:
+            _type_: _description_
+        """
+
+        t = self.time_embeddings(t).to(t.dtype)
+        t = self.time_mlp(t)
+
+        x = pack([x, mu], "b * t")[0]
+
+        if spks is not None:
+            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+            x = pack([x, spks], "b * t")[0]
+        if cond is not None:
+            x = pack([x, cond], "b * t")[0]
+
+        hiddens = []
+        masks = [mask]
+
+        down_blocks_kv_cache_new = torch.zeros(1, 4, 2, x.size(2), 512, 2).to(x.device)
+        mid_blocks_kv_cache_new = torch.zeros(12, 4, 2, x.size(2), 512, 2).to(x.device)
+        up_blocks_kv_cache_new = torch.zeros(1, 4, 2, x.size(2), 512, 2).to(x.device)
+        for index, (resnet, transformer_blocks, downsample) in enumerate(self.down_blocks):
+            mask_down = masks[-1]
+            x, down_blocks_conv_cache[index][:, :320], down_blocks_conv_cache[index][:, 320: 576] = resnet(x, mask_down, t, down_blocks_conv_cache[index][:, :320], down_blocks_conv_cache[index][:, 320: 576])
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = torch.ones(x.size(0), x.size(1), x.size(1) + down_blocks_kv_cache.size(3), device=x.device).bool()
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for i, transformer_block in enumerate(transformer_blocks):
+                x, down_blocks_kv_cache_new[index, i] = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                    cache=down_blocks_kv_cache[index, i],
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            hiddens.append(x)  # Save hidden states for skip connections
+            x, down_blocks_conv_cache[index][:, 576:] = downsample(x * mask_down, down_blocks_conv_cache[index][:, 576:])
+            masks.append(mask_down[:, :, ::2])
+        masks = masks[:-1]
+        mask_mid = masks[-1]
+
+        for index, (resnet, transformer_blocks) in enumerate(self.mid_blocks):
+            x, mid_blocks_conv_cache[index][:, :256], mid_blocks_conv_cache[index][:, 256:] = resnet(x, mask_mid, t, mid_blocks_conv_cache[index][:, :256], mid_blocks_conv_cache[index][:, 256:])
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = torch.ones(x.size(0), x.size(1), x.size(1) + mid_blocks_kv_cache.size(3), device=x.device).bool()
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for i, transformer_block in enumerate(transformer_blocks):
+                x, mid_blocks_kv_cache_new[index, i] = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                    cache=mid_blocks_kv_cache[index, i]
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+
+        for index, (resnet, transformer_blocks, upsample) in enumerate(self.up_blocks):
+            mask_up = masks.pop()
+            skip = hiddens.pop()
+            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+            x, up_blocks_conv_cache[index][:, :512], up_blocks_conv_cache[index][:, 512: 768] = resnet(x, mask_up, t, up_blocks_conv_cache[index][:, :512], up_blocks_conv_cache[index][:, 512: 768])
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = torch.ones(x.size(0), x.size(1), x.size(1) + up_blocks_kv_cache.size(3), device=x.device).bool()
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for i, transformer_block in enumerate(transformer_blocks):
+                x, up_blocks_kv_cache_new[index, i] = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                    cache=up_blocks_kv_cache[index, i]
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            x, up_blocks_conv_cache[index][:, 768:] = upsample(x * mask_up, up_blocks_conv_cache[index][:, 768:])
+        x, final_blocks_conv_cache = self.final_block(x, mask_up, final_blocks_conv_cache)
+        output = self.final_proj(x * mask_up)
+        return output * mask, down_blocks_conv_cache, down_blocks_kv_cache_new, mid_blocks_conv_cache, mid_blocks_kv_cache_new, up_blocks_conv_cache, up_blocks_kv_cache_new, final_blocks_conv_cache

cosyvoice/flow/flow.py

@@ -91,6 +91,7 @@ class MaskedDiffWithXvec(torch.nn.Module):
         conds = conds.transpose(1, 2)
 
         mask = (~make_pad_mask(feat_len)).to(h)
+        # NOTE 这一句应该是不需要的，应该h已经过length_regulator跟feat一样的shape了
         feat = F.interpolate(feat.unsqueeze(dim=1), size=h.shape[1:], mode="nearest").squeeze(dim=1)
         loss, _ = self.decoder.compute_loss(
             feat.transpose(1, 2).contiguous(),
@@ -190,6 +191,49 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
         self.token_mel_ratio = token_mel_ratio
         self.pre_lookahead_len = pre_lookahead_len
 
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        token = batch['speech_token'].to(device)
+        token_len = batch['speech_token_len'].to(device)
+        feat = batch['speech_feat'].to(device)
+        feat_len = batch['speech_feat_len'].to(device)
+        embedding = batch['embedding'].to(device)
+
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h, h_lengths = self.encoder(token, token_len)
+        h = self.encoder_proj(h)
+
+        # get conditions
+        feat = F.interpolate(feat.unsqueeze(dim=1), size=h.shape[1:], mode="nearest").squeeze(dim=1)
+        conds = torch.zeros(feat.shape, device=token.device)
+        for i, j in enumerate(feat_len):
+            if random.random() < 0.5:
+                continue
+            index = random.randint(0, int(0.3 * j))
+            conds[i, :index] = feat[i, :index]
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h)
+        loss, _ = self.decoder.compute_loss(
+            feat.transpose(1, 2).contiguous(),
+            mask.unsqueeze(1),
+            h.transpose(1, 2).contiguous(),
+            embedding,
+            cond=conds
+        )
+        return {'loss': loss}
+
     @torch.inference_mode()
     def inference(self,
                   token,
@@ -199,6 +243,7 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
                   prompt_feat,
                   prompt_feat_len,
                   embedding,
+                  cache,
                   finalize):
         if self.fp16 is True:
             prompt_feat = prompt_feat.half()
@@ -215,9 +260,17 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
         token = self.input_embedding(torch.clamp(token, min=0)) * mask
 
         # text encode
-        h, h_lengths = self.encoder(token, token_len)
-        if finalize is False:
-            h = h[:, :-self.pre_lookahead_len * self.token_mel_ratio]
+        if finalize is True:
+            h, h_lengths, encoder_cache = self.encoder.forward_chunk(token, token_len, **cache['encoder_cache'])
+        else:
+            token, context = token[:, :-self.pre_lookahead_len], token[:, -self.pre_lookahead_len:]
+            h, h_lengths, encoder_cache = self.encoder.forward_chunk(token, token_len, context=context, **cache['encoder_cache'])
+        cache['encoder_cache']['offset'] = encoder_cache[0]
+        cache['encoder_cache']['pre_lookahead_layer_conv2_cache'] = encoder_cache[1]
+        cache['encoder_cache']['encoders_kv_cache'] = encoder_cache[2]
+        cache['encoder_cache']['upsample_offset'] = encoder_cache[3]
+        cache['encoder_cache']['upsample_conv_cache'] = encoder_cache[4]
+        cache['encoder_cache']['upsample_kv_cache'] = encoder_cache[5]
         mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
         h = self.encoder_proj(h)
 
@@ -227,13 +280,14 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
         conds = conds.transpose(1, 2)
 
         mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
-        feat, _ = self.decoder(
+        feat, cache['decoder_cache'] = self.decoder(
             mu=h.transpose(1, 2).contiguous(),
             mask=mask.unsqueeze(1),
             spks=embedding,
             cond=conds,
-            n_timesteps=10
+            n_timesteps=10,
+            cache=cache['decoder_cache']
         )
         feat = feat[:, :, mel_len1:]
         assert feat.shape[2] == mel_len2
-        return feat.float(), None
+        return feat.float(), cache

cosyvoice/flow/flow_matching.py

@@ -34,7 +34,7 @@ class ConditionalCFM(BASECFM):
         self.lock = threading.Lock()
 
     @torch.inference_mode()
-    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)):
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, cache=torch.zeros(1, 80, 0, 2)):
         """Forward diffusion
 
         Args:
@@ -54,19 +54,19 @@ class ConditionalCFM(BASECFM):
         """
 
         z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature
-        cache_size = flow_cache.shape[2]
+        cache_size = cache.shape[2]
         # fix prompt and overlap part mu and z
         if cache_size != 0:
-            z[:, :, :cache_size] = flow_cache[:, :, :, 0]
-            mu[:, :, :cache_size] = flow_cache[:, :, :, 1]
+            z[:, :, :cache_size] = cache[:, :, :, 0]
+            mu[:, :, :cache_size] = cache[:, :, :, 1]
         z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
         mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
-        flow_cache = torch.stack([z_cache, mu_cache], dim=-1)
+        cache = torch.stack([z_cache, mu_cache], dim=-1)
 
         t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
         if self.t_scheduler == 'cosine':
             t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
-        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), flow_cache
+        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), cache
 
     def solve_euler(self, x, t_span, mu, mask, spks, cond):
         """
@@ -123,7 +123,7 @@ class ConditionalCFM(BASECFM):
 
     def forward_estimator(self, x, mask, mu, t, spks, cond):
         if isinstance(self.estimator, torch.nn.Module):
-            return self.estimator.forward(x, mask, mu, t, spks, cond)
+            return self.estimator(x, mask, mu, t, spks, cond)
         else:
             with self.lock:
                 self.estimator.set_input_shape('x', (2, 80, x.size(2)))
@@ -181,6 +181,9 @@ class ConditionalCFM(BASECFM):
 
         pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
         loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
+        if loss.isnan():
+            print(123)
+            pred_new = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
         return loss, y
 
 
@@ -190,7 +193,7 @@ class CausalConditionalCFM(ConditionalCFM):
         self.rand_noise = torch.randn([1, 80, 50 * 300])
 
     @torch.inference_mode()
-    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, cache={}):
         """Forward diffusion
 
         Args:
@@ -209,9 +212,105 @@ class CausalConditionalCFM(ConditionalCFM):
                 shape: (batch_size, n_feats, mel_timesteps)
         """
 
-        z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature
+        offset = cache.pop('offset')
+        z = self.rand_noise[:, :, :mu.size(2) + offset].to(mu.device).to(mu.dtype) * temperature
+        z = z[:, :, offset:]
+        offset += mu.size(2)
         # fix prompt and overlap part mu and z
         t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
         if self.t_scheduler == 'cosine':
             t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
-        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), None
+        mel, cache = self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond, cache=cache)
+        cache['offset'] = offset
+        return mel, cache
+
+    def solve_euler(self, x, t_span, mu, mask, spks, cond, cache):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+        t = t.unsqueeze(dim=0)
+
+        # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+        # Or in future might add like a return_all_steps flag
+        sol = []
+
+        # estimator cache for each step
+        down_blocks_kv_cache_new = torch.zeros(10, 1, 4, 2, x.size(2), 512, 2).to(x.device)
+        mid_blocks_kv_cache_new = torch.zeros(10, 12, 4, 2, x.size(2), 512, 2).to(x.device)
+        up_blocks_kv_cache_new = torch.zeros(10, 1, 4, 2, x.size(2), 512, 2).to(x.device)
+
+        # Do not use concat, it may cause memory format changed and trt infer with wrong results!
+        x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+        mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=x.dtype)
+        mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+        t_in = torch.zeros([2], device=x.device, dtype=x.dtype)
+        spks_in = torch.zeros([2, 80], device=x.device, dtype=x.dtype)
+        cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+        for step in range(1, len(t_span)):
+            # Classifier-Free Guidance inference introduced in VoiceBox
+            x_in[:] = x
+            mask_in[:] = mask
+            mu_in[0] = mu
+            t_in[:] = t.unsqueeze(0)
+            spks_in[0] = spks
+            cond_in[0] = cond
+            cache_step = {k: v[step - 1] for k, v in cache.items()}
+            dphi_dt, cache_step = self.forward_estimator(
+                x_in, mask_in,
+                mu_in, t_in,
+                spks_in,
+                cond_in,
+                cache_step
+            )
+            cache['down_blocks_conv_cache'][step - 1] = cache_step[0]
+            down_blocks_kv_cache_new[step - 1] = cache_step[1]
+            cache['mid_blocks_conv_cache'][step - 1] = cache_step[2]
+            mid_blocks_kv_cache_new[step - 1] = cache_step[3]
+            cache['up_blocks_conv_cache'][step - 1] = cache_step[4]
+            up_blocks_kv_cache_new[step - 1] = cache_step[5]
+            cache['final_blocks_conv_cache'][step - 1] = cache_step[6]
+            dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0)
+            dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt)
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+        cache['down_blocks_kv_cache'] = torch.concat([cache['down_blocks_kv_cache'], down_blocks_kv_cache_new], dim=4)
+        cache['mid_blocks_kv_cache'] = torch.concat([cache['mid_blocks_kv_cache'], mid_blocks_kv_cache_new], dim=4)
+        cache['up_blocks_kv_cache'] = torch.concat([cache['up_blocks_kv_cache'], up_blocks_kv_cache_new], dim=4)
+        return sol[-1].float(), cache
+
+    def forward_estimator(self, x, mask, mu, t, spks, cond, cache):
+        if isinstance(self.estimator, torch.nn.Module):
+            x, cache1, cache2, cache3, cache4, cache5, cache6, cache7 = self.estimator.forward_chunk(x, mask, mu, t, spks, cond, **cache)
+            cache = (cache1, cache2, cache3, cache4, cache5, cache6, cache7)
+        else:
+            with self.lock:
+                self.estimator.set_input_shape('x', (2, 80, x.size(2)))
+                self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
+                self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
+                self.estimator.set_input_shape('t', (2,))
+                self.estimator.set_input_shape('spks', (2, 80))
+                self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
+                # run trt engine
+                self.estimator.execute_v2([x.contiguous().data_ptr(),
+                                           mask.contiguous().data_ptr(),
+                                           mu.contiguous().data_ptr(),
+                                           t.contiguous().data_ptr(),
+                                           spks.contiguous().data_ptr(),
+                                           cond.contiguous().data_ptr(),
+                                           x.data_ptr()])
+        return x, cache

cosyvoice/hifigan/discriminator.py

@@ -1,6 +1,9 @@
 import torch
 import torch.nn as nn
-from torch.nn.utils.parametrizations import weight_norm
+try:
+    from torch.nn.utils.parametrizations import weight_norm
+except ImportError:
+    from torch.nn.utils import weight_norm
 from typing import List, Optional, Tuple
 from einops import rearrange
 from torchaudio.transforms import Spectrogram

cosyvoice/hifigan/f0_predictor.py

@@ -13,7 +13,10 @@
 # limitations under the License.
 import torch
 import torch.nn as nn
-from torch.nn.utils.parametrizations import weight_norm
+try:
+    from torch.nn.utils.parametrizations import weight_norm
+except ImportError:
+    from torch.nn.utils import weight_norm
 
 
 class ConvRNNF0Predictor(nn.Module):

cosyvoice/hifigan/generator.py

@@ -23,7 +23,10 @@ import torch.nn.functional as F
 from torch.nn import Conv1d
 from torch.nn import ConvTranspose1d
 from torch.nn.utils import remove_weight_norm
-from torch.nn.utils.parametrizations import weight_norm
+try:
+    from torch.nn.utils.parametrizations import weight_norm
+except ImportError:
+    from torch.nn.utils import weight_norm
 from torch.distributions.uniform import Uniform
 
 from cosyvoice.transformer.activation import Snake

cosyvoice/transformer/embedding.py

@@ -287,8 +287,16 @@ class EspnetRelPositionalEncoding(torch.nn.Module):
         Returns:
             torch.Tensor: Corresponding encoding
         """
-        pos_emb = self.pe[
-            :,
-            self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size,
-        ]
+        # How to subscript a Union type:
+        #   https://github.com/pytorch/pytorch/issues/69434
+        if isinstance(offset, int):
+            pos_emb = self.pe[
+                :,
+                self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset,
+            ]
+        elif isinstance(offset, torch.Tensor):
+            pos_emb = self.pe[
+                :,
+                self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset,
+            ]
         return pos_emb

cosyvoice/transformer/upsample_encoder.py

@@ -56,11 +56,16 @@ class Upsample1D(nn.Module):
         # In this mode, first repeat interpolate, than conv with stride=1
         self.conv = nn.Conv1d(self.channels, self.out_channels, stride * 2 + 1, stride=1, padding=0)
 
-    def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor):
+    def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor, conv_cache: torch.Tensor=torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         outputs = F.interpolate(inputs, scale_factor=float(self.stride), mode="nearest")
-        outputs = F.pad(outputs, (self.stride * 2, 0), value=0.0)
+        if conv_cache.size(2) == 0:
+            outputs = F.pad(outputs, (self.stride * 2, 0), value=0.0)
+        else:
+            assert conv_cache.size(2) == self.stride * 2
+            outputs = torch.concat([conv_cache, outputs], dim=2)
+        conv_cache_new = outputs[:, :, -self.stride * 2:]
         outputs = self.conv(outputs)
-        return outputs, input_lengths * self.stride
+        return outputs, input_lengths * self.stride, conv_cache_new
 
 
 class PreLookaheadLayer(nn.Module):
@@ -78,22 +83,32 @@ class PreLookaheadLayer(nn.Module):
             kernel_size=3, stride=1, padding=0,
         )
 
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+    def forward(self, inputs: torch.Tensor, context: torch.Tensor = torch.zeros(0, 0, 0), conv2_cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
         """
         inputs: (batch_size, seq_len, channels)
         """
         outputs = inputs.transpose(1, 2).contiguous()
+        context = context.transpose(1, 2).contiguous()
         # look ahead
-        outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0.0)
+        if context.size(2) == 0:
+            outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0.0)
+        else:
+            assert context.size(2) == self.pre_lookahead_len
+            outputs = F.pad(torch.concat([outputs, context], dim=2), (0, self.pre_lookahead_len - context.size(2)), mode='constant', value=0.0)
         outputs = F.leaky_relu(self.conv1(outputs))
         # outputs
-        outputs = F.pad(outputs, (2, 0), mode='constant', value=0.0)
+        if conv2_cache.size(2) == 0:
+            outputs = F.pad(outputs, (self.conv2.kernel_size[0] - 1, 0), mode='constant', value=0.0)
+        else:
+            assert conv2_cache.size(2) == self.conv2.kernel_size[0] - 1
+            outputs = torch.concat([conv2_cache, outputs], dim=2)
+        conv2_cache_new = outputs[:, :, -(self.conv2.kernel_size[0] - 1):]
         outputs = self.conv2(outputs)
         outputs = outputs.transpose(1, 2).contiguous()
 
         # residual connection
         outputs = outputs + inputs
-        return outputs
+        return outputs, conv2_cache_new
 
 
 class UpsampleConformerEncoder(torch.nn.Module):
@@ -277,12 +292,12 @@ class UpsampleConformerEncoder(torch.nn.Module):
                                               self.static_chunk_size,
                                               num_decoding_left_chunks)
         # lookahead + conformer encoder
-        xs = self.pre_lookahead_layer(xs)
+        xs, _ = self.pre_lookahead_layer(xs)
         xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)
 
         # upsample + conformer encoder
         xs = xs.transpose(1, 2).contiguous()
-        xs, xs_lens = self.up_layer(xs, xs_lens)
+        xs, xs_lens, _ = self.up_layer(xs, xs_lens)
         xs = xs.transpose(1, 2).contiguous()
         T = xs.size(1)
         masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)  # (B, 1, T)
@@ -316,3 +331,99 @@ class UpsampleConformerEncoder(torch.nn.Module):
         for layer in self.up_encoders:
             xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
         return xs
+
+    @torch.jit.export
+    def forward_chunk(
+        self,
+        xs: torch.Tensor,
+        xs_lens: torch.Tensor,
+        offset: int = 0,
+        context: torch.Tensor = torch.zeros(0, 0, 0),
+        pre_lookahead_layer_conv2_cache: torch.Tensor = torch.zeros(0, 0, 0),
+        encoders_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0),
+        upsample_offset: int = 0,
+        upsample_conv_cache: torch.Tensor = torch.zeros(0, 0, 0),
+        upsample_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0)
+    ) -> Tuple[torch.Tensor, torch.Tensor, Tuple[int, torch.Tensor, torch.Tensor, int, torch.Tensor, torch.Tensor]]:
+        """Embed positions in tensor.
+
+        Args:
+            xs: padded input tensor (B, T, D)
+            xs_lens: input length (B)
+            decoding_chunk_size: decoding chunk size for dynamic chunk
+                0: default for training, use random dynamic chunk.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+            num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+                >=0: use num_decoding_left_chunks
+                <0: use all left chunks
+        Returns:
+            encoder output tensor xs, and subsampled masks
+            xs: padded output tensor (B, T' ~= T/subsample_rate, D)
+            masks: torch.Tensor batch padding mask after subsample
+                (B, 1, T' ~= T/subsample_rate)
+        NOTE(xcsong):
+            We pass the `__call__` method of the modules instead of `forward` to the
+            checkpointing API because `__call__` attaches all the hooks of the module.
+            https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
+        """
+        assert xs.size(0) == 1
+        # tmp_masks is just for interface compatibility
+        tmp_masks = torch.ones(1,
+                               xs.size(1),
+                               device=xs.device,
+                               dtype=torch.bool)
+        tmp_masks = tmp_masks.unsqueeze(1)
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+        # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
+        xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
+        offset += xs.size(1)
+        tmp_masks = torch.ones(1,
+                               context.size(1),
+                               device=context.device,
+                               dtype=torch.bool)
+        tmp_masks = tmp_masks.unsqueeze(1)
+        if context.size(1) != 0:
+            context, _, _ = self.embed(context, tmp_masks, offset)
+
+        # lookahead + conformer encoder
+        xs, pre_lookahead_layer_conv2_cache = self.pre_lookahead_layer(xs, context, pre_lookahead_layer_conv2_cache)
+        # NOTE in cache mode we do not need to call add_optional_chunk_mask
+        chunk_masks = torch.ones((1, xs.size(1), offset), dtype=torch.bool, device=xs.device)
+        mask_pad = torch.ones((0, 0, 0), dtype=torch.bool, device=xs.device)
+        encoders_kv_cache_list = []
+        for index, layer in enumerate(self.encoders):
+            xs, chunk_masks, encoders_kv_cache_new, _ = layer(xs, chunk_masks, pos_emb, mask_pad, encoders_kv_cache[index])
+        encoders_kv_cache = torch.stack(encoders_kv_cache_list, dim=0)
+
+        # upsample
+        xs = xs.transpose(1, 2).contiguous()
+        xs, xs_lens, upsample_conv_cache = self.up_layer(xs, xs_lens, upsample_conv_cache)
+        xs = xs.transpose(1, 2).contiguous()
+
+        # tmp_masks is just for interface compatibility
+        tmp_masks = torch.ones(1,
+                               xs.size(1),
+                               device=xs.device,
+                               dtype=torch.bool)
+        tmp_masks = tmp_masks.unsqueeze(1)
+        xs, pos_emb, masks = self.up_embed(xs, tmp_masks, upsample_offset)
+        upsample_offset += xs.size(1)
+
+        # conformer encoder
+        chunk_masks = torch.ones((1, xs.size(1), upsample_offset), dtype=torch.bool, device=xs.device)
+        mask_pad = torch.ones((0, 0, 0), dtype=torch.bool, device=xs.device)
+        upsample_kv_cache_list = []
+        for index, layer in enumerate(self.up_encoders):
+            xs, chunk_masks, upsample_kv_cache_new, _ = layer(xs, chunk_masks, pos_emb, mask_pad, upsample_kv_cache[index])
+            upsample_kv_cache_list.append(upsample_kv_cache_new)
+        upsample_kv_cache = torch.stack(upsample_kv_cache_list, dim=0)
+
+        if self.normalize_before:
+            xs = self.after_norm(xs)
+        # Here we assume the mask is not changed in encoder layers, so just
+        # return the masks before encoder layers, and the masks will be used
+        # for cross attention with decoder later
+        return xs, masks, (offset, pre_lookahead_layer_conv2_cache, encoders_kv_cache_new, upsample_offset, upsample_conv_cache, upsample_kv_cache_new)

cosyvoice/utils/mask.py

@@ -87,7 +87,7 @@ def subsequent_mask(
     return mask
 
 
-def subsequent_chunk_mask_deprecated(
+def subsequent_chunk_mask(
         size: int,
         chunk_size: int,
         num_left_chunks: int = -1,
@@ -125,41 +125,6 @@ def subsequent_chunk_mask_deprecated(
     return ret
 
 
-def subsequent_chunk_mask(
-        size: int,
-        chunk_size: int,
-        num_left_chunks: int = -1,
-        device: torch.device = torch.device("cpu"),
-) -> torch.Tensor:
-    """Create mask for subsequent steps (size, size) with chunk size,
-       this is for streaming encoder
-
-    Args:
-        size (int): size of mask
-        chunk_size (int): size of chunk
-        num_left_chunks (int): number of left chunks
-            <0: use full chunk
-            >=0: use num_left_chunks
-        device (torch.device): "cpu" or "cuda" or torch.Tensor.device
-
-    Returns:
-        torch.Tensor: mask
-
-    Examples:
-        >>> subsequent_chunk_mask(4, 2)
-        [[1, 1, 0, 0],
-         [1, 1, 0, 0],
-         [1, 1, 1, 1],
-         [1, 1, 1, 1]]
-    """
-    # NOTE this modified implementation meets onnx export requirements, but it doesn't support num_left_chunks
-    # actually this is not needed after we have inference cache implemented, will remove it later
-    pos_idx = torch.arange(size, device=device)
-    block_value = (torch.div(pos_idx, chunk_size, rounding_mode='trunc') + 1) * chunk_size
-    ret = pos_idx.unsqueeze(0) < block_value.unsqueeze(1)
-    return ret
-
-
 def add_optional_chunk_mask(xs: torch.Tensor,
                             masks: torch.Tensor,
                             use_dynamic_chunk: bool,