feat: use gelu_tanh_and_mul (#1193)

python/sglang/test/test_activation.py

@@ -0,0 +1,55 @@
+import itertools
+import unittest
+
+import torch
+
+from sglang.srt.layers.activation import GeluAndMul
+
+
+class TestGeluAndMul(unittest.TestCase):
+    DTYPES = [torch.half, torch.bfloat16]
+    NUM_TOKENS = [7, 83, 2048]
+    D = [512, 4096, 5120, 13824]
+    SEEDS = [0]
+
+    @classmethod
+    def setUpClass(cls):
+        if not torch.cuda.is_available():
+            raise unittest.SkipTest("CUDA is not available")
+        torch.set_default_device("cuda")
+
+    def _run_gelu_and_mul_test(self, num_tokens, d, dtype, seed):
+        torch.manual_seed(seed)
+
+        layer = GeluAndMul().to(dtype=dtype)
+        x = torch.randn(num_tokens, 2 * d, dtype=dtype)
+
+        with torch.inference_mode():
+            ref_out = layer.forward_native(x)
+            out = layer.forward_cuda(x)
+
+        if dtype == torch.bfloat16:
+            atol = rtol = 1e-2
+        else:
+            atol = rtol = 1e-3
+
+        self.assertTrue(torch.allclose(out, ref_out, atol=atol, rtol=rtol))
+
+    def test_gelu_and_mul(self):
+        for params in itertools.product(
+            self.NUM_TOKENS,
+            self.D,
+            self.DTYPES,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                num_tokens=params[0],
+                d=params[1],
+                dtype=params[2],
+                seed=params[3],
+            ):
+                self._run_gelu_and_mul_test(*params)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)