Export Kokoro until LSTM

examples/models/kokoro/export.py

@@ -0,0 +1,53 @@
+from kokoro import KModel
+import torch
+from torch import nn
+from torch.export import default_decompositions, Dim, export_for_training
+
+torch.manual_seed(42)
+
+class WrappedModel(nn.Module):
+    def __init__(self, model: nn.Module) -> None:
+        super().__init__()
+        self.model = model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        ref_s: torch.FloatTensor,
+        speed: float = 1
+    ) -> tuple[torch.FloatTensor, torch.LongTensor]:
+        return self.model.forward_with_tokens(
+            input_ids,
+            ref_s,
+            speed,
+        )
+
+repo_id = "hexgrad/Kokoro-82M"
+model = KModel(repo_id=repo_id).eval()
+wrapped_model = WrappedModel(model)
+
+input_ids = torch.randint(1, 100, (48,))
+input_ids = torch.LongTensor([[0, *input_ids, 0]])  # S = [1, 50]
+style = torch.randn(1, 256)
+speed = torch.randint(1, 10, (1,)).int()
+example_inputs = (input_ids, style, speed)
+
+"""
+Original model output is:
+(tensor([-0.1578,  0.0960,  0.0831,  ...,  0.1224, -0.0831,  0.1492]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 2]))
+"""
+print(wrapped_model(*example_inputs))
+
+
+dynamic_shapes = {
+    # "input_ids": {0: 1, 1: Dim("input_ids", min=2, max=100)},
+    "input_ids": {},
+    "ref_s": {},
+    "speed":{},
+}
+
+exported_program = export_for_training(wrapped_model, args=example_inputs, dynamic_shapes=dynamic_shapes, strict=True)
+exported_program = exported_program.run_decompositions(default_decompositions())
+exported_program.run_decompositions()