当前Convention未适配3.10([T]泛型注解导致的问题)

cosyvoice/bin/export_jit.py

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+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 __future__ import print_function
+
+import argparse
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+import os
+import sys
+import torch
+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():
+    parser = argparse.ArgumentParser(description='export your model for deployment')
+    parser.add_argument('--model_dir',
+                        type=str,
+                        default='pretrained_models/CosyVoice-300M',
+                        help='local path')
+    args = parser.parse_args()
+    print(args)
+    return args
+
+
+def get_optimized_script(model, preserved_attrs=[]):
+    script = torch.jit.script(model)
+    if preserved_attrs != []:
+        script = torch.jit.freeze(script, preserved_attrs=preserved_attrs)
+    else:
+        script = torch.jit.freeze(script)
+    script = torch.jit.optimize_for_inference(script)
+    return script
+
+
+def main():
+    args = get_args()
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)s %(levelname)s %(message)s')
+
+    torch._C._jit_set_fusion_strategy([('STATIC', 1)])
+    torch._C._jit_set_profiling_mode(False)
+    torch._C._jit_set_profiling_executor(False)
+
+    try:
+        model = CosyVoice(args.model_dir)
+    except Exception:
+        try:
+            model = CosyVoice2(args.model_dir)
+        except Exception:
+            raise TypeError('no valid model_type!')
+
+    if not isinstance(model, CosyVoice2):
+        # 1. export llm text_encoder
+        llm_text_encoder = model.model.llm.text_encoder
+        script = get_optimized_script(llm_text_encoder)
+        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
+        script = get_optimized_script(llm_llm, ['forward_chunk'])
+        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))
+        logging.info('successfully export flow_encoder')
+    else:
+        # 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')
+
+
+if __name__ == '__main__':
+    main()