Cherrypick #3424 for release/2.7 (#3484)

Author: zewenli98

.github/workflows/build-test-linux.yml

@@ -142,6 +142,7 @@ jobs:
         python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_converters_test_results.xml -n 4 conversion/
         python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_converters_test_results.xml automatic_plugin/test_automatic_plugin.py
         python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_converters_test_results.xml automatic_plugin/test_automatic_plugin_with_attrs.py
+        python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_converters_test_results.xml automatic_plugin/test_flashinfer_rmsnorm.py
         popd
 
   tests-py-dynamo-fe:

tests/py/dynamo/automatic_plugin/test_automatic_plugin.py

@@ -81,12 +81,3 @@ def forward(self, lhs, rhs):
 
 if __name__ == "__main__":
     run_tests()
-
-# Example Usage
-# A = torch.full((64, 64), 2, device="cuda", dtype=torch.float)
-# B = torch.full((64, 64), 3, device="cuda", dtype=torch.float)
-
-# C, D = torch.ops.torchtrt_ex.elementwise_add_mul.default(A, B)
-
-# print("C (Addition):", C)
-# print("D (Multiplication):", D)

tests/py/dynamo/automatic_plugin/test_flashinfer_rmsnorm.py

@@ -0,0 +1,52 @@
+import pytest
+
+flashinfer = pytest.importorskip("flashinfer")
+import torch
+import torch.nn as nn
+import torch_tensorrt
+from parameterized import parameterized
+from torch.testing._internal.common_utils import run_tests
+from torch_tensorrt._enums import dtype
+
+from ..conversion.harness import DispatchTestCase
+
+
+@torch.library.custom_op("flashinfer::rmsnorm", mutates_args=())  # type: ignore[misc]
+def flashinfer_rmsnorm(
+    input: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6
+) -> torch.Tensor:
+    return flashinfer.norm.rmsnorm(input, weight)
+
+
+@torch.library.register_fake("flashinfer::rmsnorm")
+def _(input: torch.Tensor, weight: torch.Tensor, b: float = 1e-6) -> torch.Tensor:
+    return input
+
+
+torch_tensorrt.dynamo.conversion.plugins.custom_op(
+    "flashinfer::rmsnorm", supports_dynamic_shapes=True
+)
+
+
+class TestAutomaticPlugin(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ((64, 64), (64,), torch.float16),
+            ((256, 256), (256,), torch.float16),
+        ]
+    )
+    def test_rmsnorm_float(self, input_shape, weight_shape, data_type):
+        class rmsnorm(nn.Module):
+            def forward(self, input, weight):
+                return torch.ops.flashinfer.rmsnorm.default(input, weight)
+
+        inputs = [
+            torch.randn(input_shape, device="cuda", dtype=data_type),
+            torch.randn(weight_shape, device="cuda", dtype=data_type),
+        ]
+
+        self.run_test(rmsnorm(), inputs, precision=dtype.f16)
+
+
+if __name__ == "__main__":
+    run_tests()