Fix apply_compile called multiple times in PP initialization

tests/unit_tests/test_compile.py

@@ -0,0 +1,81 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import torch
+import torch.nn as nn
+
+from torchtitan.config.job_config import Compile as CompileConfig
+from torchtitan.models.llama4.infra.parallelize import apply_compile
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, dim=512):
+        super().__init__()
+        self.attention = nn.Linear(dim, dim, bias=False)
+        self.mlp = nn.Linear(dim, dim, bias=False)
+        self.moe_enabled = False
+
+    def forward(self, x):
+        x = self.attention(x)
+        x = self.mlp(x)
+        return x
+
+
+class TinyModel(nn.Module):
+    def __init__(self, num_layers=2, dim=512):
+        super().__init__()
+        self.layers = nn.ModuleDict(
+            {str(i): TransformerBlock(dim) for i in range(num_layers)}
+        )
+
+    def forward(self, x):
+        for layer in self.layers.values():
+            x = layer(x)
+        return x
+
+
+class TestApplyCompile(unittest.TestCase):
+    def test_patched_once(self):
+        """
+        Calls apply_compile multiple times, as in the case with PP.
+        But patches should only happen once
+        """
+        unused_model1 = TinyModel(num_layers=2, dim=128)
+        unused_model2 = TinyModel(num_layers=2, dim=128)
+        compile_config = CompileConfig(backend="eager")
+
+        apply_compile(unused_model1, compile_config, ep_enabled=True)
+        apply_compile(unused_model2, compile_config, ep_enabled=True)
+
+        from torchtitan.models.moe import moe as moe_module
+
+        # Generate sample inputs for _run_experts_grouped_mm
+        num_experts = 8
+        dim = 128
+        hidden_dim = 256
+        w1 = torch.randn(num_experts, hidden_dim, dim)
+        w2 = torch.randn(num_experts, dim, hidden_dim)
+        w3 = torch.randn(num_experts, hidden_dim, dim)
+        num_tokens_per_expert = torch.tensor(
+            [10, 8, 12, 9, 11, 7, 10, 13], dtype=torch.int32
+        )
+        total_tokens = num_tokens_per_expert.sum().item()
+        x = torch.randn(total_tokens, dim)
+
+        # Call the function, should not error
+        output = moe_module._run_experts_grouped_mm(
+            w1, w2, w3, x, num_tokens_per_expert
+        )
+
+        print(f"Input shape: {x.shape}")
+        print(f"Output shape: {output.shape}")
+        print(f"Num tokens per expert: {num_tokens_per_expert}")
+
+
+if __name__ == "__main__":
+    unittest.main()

torchtitan/models/llama4/infra/parallelize.py

@@ -572,27 +572,34 @@ def apply_compile(model: nn.Module, compile_config: CompileConfig, ep_enabled: b
 
         model.layers.register_module(layer_id, transformer_block)
 
-    moe_module._run_experts_grouped_mm = torch.compile(
-        moe_module._run_experts_grouped_mm,
-        backend=compile_config.backend,
-        fullgraph=True,
+    # Patch some globals only once (apply_compile is called multiple times for PP setup)
+    already_patched = (
+        "_run_experts_grouped_mm_dynamic"
+        in moe_module._run_experts_grouped_mm.__qualname__
     )
+    if not already_patched:
+        moe_module._run_experts_grouped_mm = torch.compile(
+            moe_module._run_experts_grouped_mm,
+            backend=compile_config.backend,
+            fullgraph=True,
+        )
 
-    if ep_enabled:
-        compiled_fn = moe_module._run_experts_grouped_mm
-
-        def _run_experts_grouped_mm_dynamic(
-            w1: torch.Tensor,
-            w2: torch.Tensor,
-            w3: torch.Tensor,
-            x: torch.Tensor,
-            num_tokens_per_expert: torch.Tensor,
-        ) -> torch.Tensor:
-            # dynamic number of tokens in expert parallel
-            torch._dynamo.mark_dynamic(x, 0)
-            return compiled_fn(w1, w2, w3, x, num_tokens_per_expert)
-
-        moe_module._run_experts_grouped_mm = _run_experts_grouped_mm_dynamic
+        if ep_enabled:
+            compiled_fn = moe_module._run_experts_grouped_mm
+
+            # keep function logic in sync with `already_patched` above
+            def _run_experts_grouped_mm_dynamic(
+                w1: torch.Tensor,
+                w2: torch.Tensor,
+                w3: torch.Tensor,
+                x: torch.Tensor,
+                num_tokens_per_expert: torch.Tensor,
+            ) -> torch.Tensor:
+                # dynamic number of tokens in expert parallel
+                torch._dynamo.mark_dynamic(x, 0)
+                return compiled_fn(w1, w2, w3, x, num_tokens_per_expert)
+
+            moe_module._run_experts_grouped_mm = _run_experts_grouped_mm_dynamic
 
     # NOTE: We don't compile for loop code path due to an issue with unbacked symints:
     # https://github.com/pytorch/pytorch/issues/166460