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export_onnx.py

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+# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, [email protected])
+# 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 onnxruntime
+import random
+import torch
+from tqdm import tqdm
+from hyperpyyaml import load_hyperpyyaml
+
+
+def get_dummy_input(batch_size, seq_len, out_channels, device):
+    x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+    mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device)
+    mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+    t = torch.rand((batch_size), dtype=torch.float32, device=device)
+    spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device)
+    cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+    return x, mask, mu, t, spks, cond
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='export your model for deployment')
+    parser.add_argument('--model_dir',
+                        type=str,
+                        default='Step-Audio-2-mini/token2wav',
+                        help='local path')
+    parser.add_argument('--onnx_model',
+                        type=str,
+                        default='flow.decoder.estimator.fp32.dynamic_batch.onnx',
+                        help='onnx model name')
+    args = parser.parse_args()
+    print(args)
+    return args
+
+
+@torch.no_grad()
+def main():
+    args = get_args()
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)s %(levelname)s %(message)s')
+
+    with open(f"{args.model_dir}/flow.yaml", "r") as f:
+        configs = load_hyperpyyaml(f)
+        flow_model = configs['flow']
+
+    device = torch.device('cuda')
+
+
+    # 1. export flow decoder estimator
+    flow_model.load_state_dict(torch.load(f"{args.model_dir}/flow.pt", map_location="cpu", weights_only=True), strict=True)
+    estimator = flow_model.decoder.estimator
+    estimator.eval()
+    estimator.to(device)
+
+
+    batch_size, seq_len = 2, 256
+    out_channels = flow_model.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),
+        f'{args.model_dir}/{args.onnx_model}',
+        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': {0: 'batch_size', 2: 'seq_len'},
+            'mask': {0: 'batch_size', 2: 'seq_len'},
+            'mu': {0: 'batch_size', 2: 'seq_len'},
+            'cond': {0: 'batch_size', 2: 'seq_len'},
+            't': {0: 'batch_size'},
+            'spks': {0: 'batch_size'},
+            'estimator_out': {0: 'batch_size', 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(f'{args.model_dir}/{args.onnx_model}',
+                                                  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)
+    logging.info('successfully export estimator')
+
+
+if __name__ == "__main__":
+    main()
+

flow.decoder.estimator.fp32.dynamic_batch.onnx

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+oid sha256:c31e8c62abb6eacdd929baf37910cb58ce71c1b16bd71ced48d807ea6697da48
+size 458962271